Generative adversarial neural network assisted reconstruction

ABSTRACT

A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person&#39;s face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.

CLAIM OF PRIORITY

-   -   This application is a continuation of U.S. patent application        Ser. No. 17/069,449 titled “Generative Adversarial Neural        Network Assisted Video Reconstruction,” filed Oct. 13, 2020 and        Ser. No. 17/069,478 titled “Generative Adversarial Neural        Network Assisted Video Compression and Broadcast,” filed Oct.        13, 2020 which each claim the benefit of U.S. Provisional        Application No. 63/010,511 titled “Generative Neural Network        Assisted Video Compression and Decompression,” filed Apr. 15,        2020, the entire contents of which is incorporated herein by        reference. U.S. patent application Ser. Nos. 17/069,449 and        17/069,478 are each a continuation-in-part of U.S. patent        application Ser. No. 16/418,317 titled “A Style-Based        Architecture For Generative Neural Networks,” filed May 21, 2019        which claims the benefit of U.S. Provisional Application No.        62/767,417 titled “A Style-Based Architecture For Generative        Neural Networks,” filed Nov. 14, 2018 and U.S. Provisional        Application No. 62/767,985 titled “A Style-Based Architecture        For Generative Neural Networks,” filed Nov. 15, 2018, the entire        contents of these applications is incorporated herein by        reference.

This application is a continuation-in-part of U.S. patent applicationSer. No. 16/418,317 titled “A Style-Based Architecture For GenerativeNeural Networks,” filed May 21, 2019 which claims the benefit of U.S.Provisional Application No. 62/767,417 titled “A Style-BasedArchitecture For Generative Neural Networks,” filed Nov. 14, 2018 andU.S. Provisional Application No. 62/767,985 titled “A Style-BasedArchitecture For Generative Neural Networks,” filed Nov. 15, 2018, theentire contents of these applications is incorporated herein byreference. This application also claims the benefit of U.S. ProvisionalApplication No. 63/010,511 titled “Generative Neural Network AssistedVideo Compression and Decompression,” filed Apr. 14, 2020, the entirecontents of which is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to neural networks, and in particular, toa generator architecture for synthesizing data using scale-specificcontrols.

BACKGROUND

The resolution and quality of images produced by generative adversarialnetworks (GAN) has improved recently. Yet GANs continue to operate asblack boxes, and despite recent efforts, the understanding of variousaspects of the image synthesis process, e.g., the origin of stochasticfeatures, is still lacking. The properties of the latent space are alsopoorly understood, and the commonly demonstrated latent spaceinterpolations provide no quantitative way to compare different GANsagainst each other. There is a need for addressing these issues and/orother issues associated with the prior art.

SUMMARY

A style-based generative network architecture enables scale-specificcontrol of synthesized output data, such as images. During training, thestyle-based generative neural network (generator neural network)includes a mapping network and a synthesis network. During prediction,the mapping network may be omitted, replicated, or evaluated severaltimes. The synthesis network may be used to generate highly varied,high-quality output data with a wide variety of attributes. For example,when used to generate images of people's faces, the attributes that mayvary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses,colors (eyes, hair, etc.), hair style, lighting, background, etc.Depending on the task, generated output data may include images, audio,video, three-dimensional (3D) objects, text, etc.

A latent code defined in an input space is processed by the mappingneural network to produce an intermediate latent code defined in anintermediate latent space. The intermediate latent code may be used asappearance vector that is processed by the synthesis neural network togenerate an image. The appearance vector is a compressed encoding ofdata, such as video frames including a person's face, audio, and otherdata. Captured images may be converted into appearance vectors at alocal device and transmitted to a remote device using much lessbandwidth compared with transmitting the captured images. A synthesisneural network at the remote device reconstructs the images for display.

A method, computer readable medium, and system are disclosed forgenerative adversarial neural network assisted video reconstruction.Replication data specific to a real or synthetic subject is obtained forconfiguring a synthesis neural network. An appearance vector is receivedthat encodes attributes of a human face captured in a frame of video.The synthesis neural network processes the appearance vector toreconstruct an image of the human face including characteristics definedby the replication data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram of a style-based generator system,in accordance with an embodiment.

FIG. 1B illustrates images generated by the style-based generatorsystem, in accordance with an embodiment.

FIG. 1C illustrates a flowchart of a method for style-based generation,in accordance with an embodiment.

FIG. 2A illustrates a block diagram of the mapping neural network shownin FIG. 1A, in accordance with an embodiment.

FIG. 2B illustrates a block diagram of the synthesis neural networkshown in FIG. 1A, in accordance with an embodiment.

FIG. 2C illustrates a flowchart of a method for applying spatial noiseusing the style-based generator system, in accordance with anembodiment.

FIG. 2D illustrates a block diagram of a GAN system, in accordance withan embodiment.

FIG. 3 illustrates a parallel processing unit, in accordance with anembodiment.

FIG. 4A illustrates a general processing cluster within the parallelprocessing unit of FIG. 3 , in accordance with an embodiment.

FIG. 4B illustrates a memory partition unit of the parallel processingunit of FIG. 3 , in accordance with an embodiment.

FIG. 5A illustrates the streaming multi-processor of FIG. 4A, inaccordance with an embodiment.

FIG. 5B is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 3 , in accordance with an embodiment.

FIG. 5C illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

FIG. 5D illustrates components of an exemplary system that can be usedto train and utilize machine learning, for use in implementing someembodiments of the present disclosure.

FIG. 6A illustrates an exemplary video streaming system suitable for usein implementing some embodiments of the present disclosure.

FIG. 6B illustrates a variety of appearance vectors for use inimplementing some embodiments of the present disclosure.

FIG. 6C illustrates a flowchart of a method for GAN-assisted videocompression, in accordance with an embodiment.

FIG. 6D illustrates a flowchart of a method for GAN-assisted videoreconstruction, in accordance with an embodiment.

FIG. 7A is a conceptual diagram of a synthesis neural network trainingconfiguration, for use in implementing some embodiments of the presentdisclosure.

FIG. 7B is a conceptual diagram of an end-to-end system including theprojector of FIG. 7A, for use in implementing some embodiments of thepresent disclosure.

FIG. 7C is a conceptual diagram of a configuration for generatingtraining data, for use in implementing some embodiments of the presentdisclosure.

FIG. 7D is a conceptual diagram of a training configuration usinglandmarks to predict appearance vectors, for use in implementing someembodiments of the present disclosure.

FIG. 7E is a conceptual diagram of another end-to-end system includingthe synthesis neural network, for use in implementing some embodimentsof the present disclosure.

FIG. 8A is a conceptual diagram of another synthesis neural networktraining configuration, for use in implementing some embodiments of thepresent disclosure.

FIG. 8B is a conceptual diagram of yet another synthesis neural networktraining configuration, for use in implementing some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

A style-based generative network architecture enables scale-specificcontrol of the synthesized output. A style-based generator systemincludes a mapping network and a synthesis network. Conceptually, in anembodiment, feature maps (containing spatially varying informationrepresenting content of the output data, where each feature map is onechannel of intermediate activations) generated by different layers ofthe synthesis network are modified based on style control signalsprovided by the mapping network. The style control signals for differentlayers of the synthesis network may be generated from the same ordifferent latent codes. As used herein, the term style control signalscontrol attributes of synthesized images of a subject such as pose,general hair style, face shape, eyeglasses, colors (eyes, hair,lighting), and microstructure. A latent code may be a randomN-dimensional vector drawn from, e.g., a Gaussian distribution. Thestyle control signals for different layers of the synthesis network maybe generated from the same or different mapping networks. Additionally,spatial noise may be injected into each layer of the synthesis network.

FIG. 1A illustrates a block diagram of a style-based generator system100, in accordance with an embodiment. The style-based generator system100 includes a mapping neural network 110, a style conversion unit 115,and a synthesis neural network 140. After the synthesis neural network140 is trained, the synthesis neural network 140 may be deployed withoutthe mapping neural network 110 when the intermediate latent code(s)and/or the style signals produced by the style conversion unit 115 arepre-computed. In an embodiment, additional style conversion units 115may be included to convert the intermediate latent code generated by themapping neural network 110 into a second style signal or to convert adifferent intermediate latent code into the second style signal. One ormore additional mapping neural networks 110 may be included in thestyle-based generator system 100 to generate additional intermediatelatent codes from the latent code or additional latent codes.

The style-based generator system 100 may be implemented by a program,custom circuitry, or by a combination of custom circuitry and a program.For example, the style-based generator system 100 may be implementedusing a GPU (graphics processing unit), CPU (central processing unit),or any processor capable of performing the operations described herein.Furthermore, persons of ordinary skill in the art will understand thatany system that performs the operations of the style-based generatorsystem 100 is within the scope and spirit of embodiments of the presentinvention.

Conventionally, a latent code is provided to a generator through aninput layer, such as the first layer of a feedforward neural network. Incontrast, in an embodiment, instead of receiving the latent code, thesynthesis neural network 140 starts from a learned constant and thelatent code is input to the mapping neural network 110. In anembodiment, the first intermediate data is the learned constant. Given alatent code z in the input latent space

, a non-linear mapping network ƒ:

→

first produces intermediate latent code w∈

. The mapping neural network 110 may be configured to implement thenon-linear mapping network. In an embodiment, the dimensions of inputand output activations in the input latent space

and the intermediate latent space

are equal (e.g., 512). In an embodiment, the mapping function ƒ isimplemented using an 8-layer MLP (multilayer perceptron, i.e., a neuralnetwork consisting of only fully-connected layers).

While the conventional generator only feeds the latent code though theinput layer of the generator, the mapping neural network 110 insteadmaps the input latent code z to the intermediate latent space

to produce the intermediate latent code w. The style conversion unit 115converts the intermediate latent code w into a first style signal. Oneor more intermediate latent codes w are converted into spatiallyinvariant styles including the first style signal and a second stylesignal. In contrast with conventional style transfer techniques, thespatially invariant styles are computed from a vector, namely theintermediate latent code w, instead of from an example image. The one ormore intermediate latent codes w may be generated by one or more mappingneural networks 110 for one or more respective latent codes z. Thesynthesis neural network 140 processes the first intermediate data(e.g., a learned constant encoded as a feature map) according to thestyle signals, for example, increasing density of the first intermediatedata from 4×4 to 8×8 and continuing until the output data density isreached.

In an embodiment, the style conversion unit 115 performs an affinetransformation. The style conversion unit 115 may be trained to learnthe affine transformation during training of the synthesis neuralnetwork 140. The first style signal controls operations at a first layer120 of the synthesis neural network 140 to produce modified firstintermediate data. In an embodiment, the first style signal controls anadaptive instance normalization (AdaIN) operation within the first layer120 of the synthesis network 140. In an embodiment, the AdaIN operationreceives a set of content feature maps and a style signal and modifiesthe first-order statistics (i.e., the “style”) of the content featuremaps to match first-order statistics defined by the style signal. Themodified first intermediate data output by the first layer 120 isprocessed by processing layer(s) 125 to generate second intermediatedata. In an embodiment, the processing layer(s) 125 include a 3×3convolution layer. In an embodiment, the processing layer(s) 125 includea 3×3 convolution layer followed by an AdaIN operation that receives anadditional style signal, not explicitly shown in FIG. 1A.

The second intermediate data is input to a second layer 130 of thesynthesis neural network 140. The second style signal controlsoperations at the second layer 130 to produce modified secondintermediate data. In an embodiment, the first style signal modifies afirst attribute encoded in the first intermediate data and the secondstyle signal modifies a second attribute encoded in the firstintermediate data and the second intermediate data. For example, thefirst intermediate data is coarse data compared with the secondintermediate data and the first style is transferred to coarse featuremaps at the first layer 120 while the second style is transferred tohigher density feature maps at the second layer 130.

In an embodiment, the second layer 130 up-samples the secondintermediate data and includes a 3×3 convolution layer followed by anAdaIN operation. In an embodiment, the second style signal controls anAdaIN operation within the second layer 130 of the synthesis network140. The modified second intermediate data output by the second layer130 is processed by processing layer(s) 135 to generate output dataincluding content corresponding to the second intermediate data. In anembodiment, multiple (e.g., 32, 48, 64, 96, etc.) channels of featuresin the modified second intermediate data are converted into the outputdata that is encoded as color channels (e.g., red, green, blue).

In an embodiment, the processing layer(s) 135 includes a 3×3 convolutionlayer. In an embodiment, the output data is an image including firstattributes corresponding to a first scale and second attributescorresponding to a second scale, where the first scale is coarsercompared with the second scale. The first scale may correspond to ascale of the feature maps processed by the first layer 120 and thesecond scale may correspond to a scale of the feature maps processed bythe second layer 130. Accordingly, the first style signal modifies thefirst attributes at the first scale and the second style signal modifiesthe second attributes at the second scale.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 1B illustrates images generated by the style-based generator system100, in accordance with an embodiment. The images are generated in 1024²resolution. In other embodiments, the images can be generated at adifferent resolution. Two different latent codes are used to control thestyles of images generated by the style-based generator system 100.Specifically, a first portion of the styles are produced by the mappingneural network 110 and a style conversion unit 115 from the “source”latent codes in the top row. A second portion of the styles are producedby the same or an additional mapping neural network 110 and acorresponding style conversion unit 115 from the “destination” latentcodes in the leftmost column. The style-based generator system 100starts from a learned constant input at the synthesis neural network 140and adjusts the “style” of the image at each convolution layer based onthe latent code, therefore directly controlling the strength of imageattributes, encoded in feature maps, at different scales. In otherwords, a given set of styles from “source” data is copied to“destination” data. Thus, the copied styles (coarse, middle, or fine)are taken from the “source” data while all the other styles are kept thesame as in the “destination” data.

The first portion of styles (destination) are applied by the synthesisneural network 140 to process the learned constant with a first subsetof the first portion of styles replaced with a corresponding secondsubset of the second portion of the styles (source). In an embodiment,the learned constant is a 4×4×512 constant tensor. In the second, third,and fourth rows of images in FIG. 1B, the second portion of the styles(source) replaces the first portion of the styles (destination) atcoarse layers of the synthesis neural network 140. In an embodiment, thecoarse layers correspond to coarse spatial densities 4²-8². In anembodiment, high-level attributes such as pose, general hair style, faceshape, and eyeglasses are copied from the source, while otherattributes, such as all colors (eyes, hair, lighting) and finer facialfeatures of the destination are retained.

In the fifth and sixth rows of images in FIG. 1B, second portion of thestyles (source) replaces the first portion of the styles (destination)at middle layers of the synthesis neural network 140. In an embodiment,the middle layers correspond to spatial densities of 16²-32². Smallerscale facial features, hair style, eyes open/closed are inherited fromthe source, while the pose, general face shape, and eyeglasses from thedestination are preserved. Finally, in the last row of images in FIG.1B, the second portion of the styles (source) replaces the first portionof the styles (destination) at high density (fine) layers of thesynthesis neural network 140. In an embodiment, the fine layerscorrespond to spatial densities of 64²-1024². Using the styles from thesecond portion of the styles (source) for the fine layers inherits thecolor scheme and microstructure from the source while preserving thepose and general face shape from the destination.

The architecture of the style-based generator system 100 enables controlof the image synthesis via scale-specific modifications to the styles.The mapping network 110 and affine transformations performed by thestyle conversion unit 115 can be viewed as a way to draw samples foreach style from a learned distribution, and the synthesis network 140provides a mechanism to generate a novel image based on a collection ofstyles. The effects of each style are localized in the synthesis network140, i.e., modifying a specific subset of the styles can be expected toaffect only certain attributes of the image.

Using style signals from at least two different latent codes, as shownin FIG. 1B, is referred to as style mixing or mixing regularization.Style mixing during training decorrelates neighboring styles and enablesmore fine-grained control over the generated imagery. In an embodiment,during training a given percentage of images are generated using tworandom latent codes instead of one. When generating such an image, arandom location (e.g., crossover point) in the synthesis neural network140 may be selected where processing switches from using style signalsgenerated using a first latent code to style signals generated using asecond latent code. In an embodiment, two latent codes z₁, z₂ areprocessed by the mapping neural network 110, and the correspondingintermediate latent codes w₁, w₂ control the styles so that w₁ appliesbefore the crossover point and w₂ after the crossover point. The mixingregularization technique prevents the synthesis neural network 140 fromassuming that adjacent styles are correlated.

TABLE 1 shows how enabling mixing regularization during training mayimprove localization of the styles considerably, indicated by improved(lower is better) Fréchet inception distances (FIDs) in scenarios wheremultiple latent codes are mixed at test time. The images shown in FIG.1B are examples of images synthesized by mixing two latent codes atvarious scales. Each subset of styles controls meaningful high-levelattributes of the image.

TABLE 1 FIDs for different mixing regularization ratios Number of latentcodes Mixing ratio (test time) (training time) 1 2 3 4  0% 4.42 8.2212.88 17.41  50% 4.41 6.10 8.71 11.61  90% 4.40 5.11 6.88 9.03 100% 4.835.17 6.63 8.40

The mixing ratio indicates that percentage of training examples forwhich mixing regularization is enabled. A maximum of four differentlatent codes were randomly selected during test time and the crossoverpoints between the different latent codes were also randomly selected.Mixing regularization improves the tolerance to these adverse operationssignificantly.

As confirmed by the FIDs, the average quality of the images generated bythe style-based generator system 100 is high, and even accessories suchas eyeglasses and hats are successfully synthesized. For the imagesshown in FIG. 1B, sampling from the extreme regions of

is avoided by using the so-called truncation trick that can be performedin

instead of

. Note that the style-based generator system 100 may be implemented toenable application of the truncation selectively to low resolutionsonly, so that high-resolution details are not affected.

Considering the distribution of training data, areas of low density arepoorly represented and thus likely to be difficult for the style-basedgenerator system 100 to learn. Non-uniform distributions of trainingdata present a significant open problem in all generative modelingtechniques. However, it is known that drawing latent vectors from atruncated or otherwise shrunk sampling space tends to improve averageimage quality, although some amount of variation is lost. In anembodiment, to improve training of the style-based generator system 100,a center of mass of

is computed as w=

_(z˜P(z))[ƒ(z)]. In the case of one dataset of human faces (e.g., FFHQ,Flickr-Faces-HQ), the point represents a sort of an average face (ψ=0).The deviation of a given w is scaled down from the center asw′=w+ψ(w−w), where ψ<1. In conventional generative modeling systems,only a subset of the neural networks are amenable to such truncation,even when orthogonal regularization is used, truncation in

space seems to work reliably even without changes to the loss function.

FIG. 1C illustrates a flowchart of a method 150 for style-basedgeneration, in accordance with an embodiment. The method 150 may beperformed by a program, custom circuitry, or by a combination of customcircuitry and a program. For example, the method 150 may be executed bya GPU (graphics processing unit), CPU (central processing unit), or anyprocessor capable of performing the operations of the style-basedgenerator system 100. Furthermore, persons of ordinary skill in the artwill understand that any system that performs method 150 is within thescope and spirit of embodiments of the present invention.

At step 155, the mapping neural network 110 processes a latent codedefined in an input space, to produce an intermediate latent codedefined in an intermediate latent space. At step 160, the intermediatelatent code is converted into a first style signal by the styleconversion unit 115. At step 165, the first style signal is applied at afirst layer 120 of the synthesis neural network 140 to modify the firstintermediate data according to the first style signal to producemodified first intermediate data. At step 170, the modified firstintermediate data is processed by the processing layer(s) 125 to producethe second intermediate data. At step 175, a second style signal isapplied at the second layer 130 of the synthesis neural network 140 tomodify the second intermediate data according to the second style signalto produce modified second intermediate data. At step 180, the modifiedsecond intermediate data is processed by the processing layer(s) 135 toproduce output data including content corresponding to the secondintermediate data.

There are various definitions for disentanglement, but a common goal isa latent space that consists of linear subspaces, each of which controlsone factor of variation. However, the sampling probability of eachcombination of factors in the latent space

needs to match the corresponding density in the training data.

A major benefit of the style-based generator system 100 is that theintermediate latent space

does not have to support sampling according to any fixed distribution;the sampling density for the style-based generator system 100 is inducedby the learned piecewise continuous mapping ƒ(z). The mapping can beadapted to “unwarp”

so that the factors of variation become more linear. The style-basedgenerator system 100 may naturally tend to unwarp

, as it should be easier to generate realistic images based on adisentangled representation than based on an entangled representation.As such, the training may yield a less entangled

in an unsupervised setting, i.e., when the factors of variation are notknown in advance.

FIG. 2A illustrates a block diagram of the mapping neural network 110shown in FIG. 1A, in accordance with an embodiment. A distribution ofthe training data may be missing a combination of attributes, such as,children wearing glasses. A distribution of the factors of variation inthe combination of glasses and age becomes more linear in theintermediate latent space

compared with the latent space

.

In an embodiment, the mapping neural network 110 includes anormalization layer 205 and multiple fully-connected layers 210. In anembodiment, eight fully-connected layers 210 are coupled in sequence toproduce the intermediate latent code. Parameters (e.g., weights) of themapping neural network 110 are learned during training and theparameters are used to process the input latent codes when thestyle-based generator system 100 is deployed to generate the outputdata. In an embodiment, the mapping neural network 110 generates one ormore intermediate latent codes that are used by the synthesis neuralnetwork 140 at a later time to generate the output data.

There are many attributes in human portraits that can be regarded asstochastic, such as the exact placement of hairs, stubble, freckles, orskin pores. Any of these can be randomized without affecting aperception of the image as long as the randomizations follow the correctdistribution. The artificial omission of noise when generating imagesleads to images with a featureless “painterly” look. In particular, whengenerating human portraits, coarse noise may cause large-scale curlingof hair and appearance of larger background features, while the finenoise may bring out the finer curls of hair, finer background detail,and skin pores.

A conventional generator may only generate stochastic variation based onthe input to the neural network, as provided through the input layer.During the training, the conventional generator may be forced to learnto generate spatially-varying pseudorandom numbers from earlieractivations whenever the pseudorandom numbers are needed. In otherwords, pseudorandom number generation is not intentionally built intothe conventional generator. Instead, the generation of pseudorandomnumbers emerges on its own during training in order for the conventionalgenerator to satisfy the training objective. Generating the pseudorandomnumbers consumes neural network capacity and hiding the periodicity ofgenerated signal is difficult—and not always successful, as evidenced bycommonly seen repetitive patterns in generated images. In contrast,style-based generator system 100 may be configured to avoid theselimitations by adding per-pixel noise after each convolution.

In an embodiment, the style-based generator system 100 is configuredwith a direct means to generate stochastic detail by introducingexplicit noise inputs. In an embodiment, the noise inputs aresingle-channel images consisting of uncorrelated Gaussian noise, and adedicated noise image is input to one or more layers of the synthesisnetwork 140. The noise image may be broadcast to all feature maps usinglearned per-feature scaling factors and then added to the output of thecorresponding convolution.

FIG. 2B illustrates a block diagram of the synthesis neural network 140shown in FIG. 1A, in accordance with an embodiment. The synthesis neuralnetwork 140 includes a first processing block 200 and a secondprocessing block 230. In an embodiment, the processing block 200processes 4×4 resolution feature maps and the processing block 230processes 8×8 resolution feature maps. One or more additional processingblocks may be included in the synthesis neural network 140 after theprocessing blocks 200 and 230, before them, and/or between them.

The first processing block 200 receives the first intermediate data,first spatial noise, and second spatial noise. In an embodiment, thefirst spatial noise is scaled by a learned per-channel scaling factorbefore being combined with (e.g., added to) the first intermediate data.In an embodiment, the first spatial noise, second spatial noise, thirdspatial noise, and fourth spatial noise are independent per-pixelGaussian noise.

The first processing block 200 also receives the first style signal andthe second style signal. As previously explained, the style signals maybe obtained by processing the intermediate latent code according to alearned affine transform. Learned affine transformations specialize w tostyles y=(y_(s), y_(b)) that control adaptive instance normalization(AdaIN) operations implemented by the modules 220 in the synthesisneural network 140. AdaIN is particularly well suited for implementationin the style-based generator system 100 due to its efficiency andcompact representation.

The AdaIN operation is defined

$\begin{matrix}{{{AdaIN}\left( {x_{i},y} \right)} = {{y_{s,i}\frac{x_{i} - {\mu\left( x_{i} \right)}}{\sigma\left( x_{i} \right)}} + y_{b,i}}} & (1)\end{matrix}$where each feature map x_(i), is normalized separately, and then scaledand biased using the corresponding scalar components from style y. Thus,the dimensionality of y is twice the number of feature maps compared tothe input of the layer. In an embodiment, a dimension of the stylesignal is a multiple of a number of feature maps in the layer at whichthe style signal is applied. In contrast with conventional styletransfer, the spatially invariant style y is computed from vector winstead of an example image.

The effects of each style signal are localized in the synthesis neuralnetwork 140, i.e., modifying a specific subset of the style signals canbe expected to affect only certain attributes of an image represented bythe output data. To see the reason for the localization, consider howthe AdaIN operation (Eq. 1) implemented by the module 220 firstnormalizes each channel to zero mean and unit variance, and only thenapplies scales and biases based on the style signal. The new per-channelstatistics, as dictated by the style, modify the relative importance offeatures for the subsequent convolution operation, but the newper-channel statistics do not depend on the original statistics becauseof the normalization. Thus, each style signal controls only apre-defined number of convolution(s) 225 before being overridden by thenext AdaIN operation. In an embodiment, scaled spatial noise is added tothe features after each convolution and before processing by anothermodule 225.

Each module 220 may be followed by a convolution layer 225. In anembodiment, the convolution layer 225 applies a 3×3 convolution kernelto the input. Within the processing block 200, second intermediate dataoutput by the convolution layer 225 is combined with the second spatialnoise and input to a second module 220 that applies the second stylesignal to generate an output of the processing block 200. In anembodiment, the second spatial noise is scaled by a learned per-channelscaling factor before being combined with (e.g., added to) the secondintermediate data.

The processing block 230 receives feature maps output by the processingblock 200 and the feature maps are up-sampled by an up-sampling layer235. In an embodiment 4×4 feature maps are up-sampled by the up-samplinglayer 235 to produce 8×8 feature maps. The up-sampled feature maps areinput to another convolution layer 225 to produce third intermediatedata. Within the processing block 230, the third intermediate data iscombined with the third spatial noise and input to a third module 220that applies the third style signal via an AdaIN operation. In anembodiment, the third spatial noise is scaled by a learned per-channelscaling factor before being combined with (e.g., added to) the thirdintermediate data. The output of the third module 220 is processed byanother convolution layer 225 to produce fourth intermediate data. Thefourth intermediate data is combined with the fourth spatial noise andinput to a fourth module 220 that applies the fourth style signal via anAdaIN operation. In an embodiment, the fourth spatial noise is scaled bya learned per-channel scaling factor before being combined with (e.g.,added to) the fourth intermediate data.

In an embodiment, a resolution of the output data is 1024² and thesynthesis neural network 140 includes 18 layers—two for eachpower-of-two resolution (4²-1024 ²). The output of the last layer of thesynthesis neural network 140 may be converted to RGB using a separate1×1 convolution. In an embodiment, the synthesis neural network 140 hasa total of 26.2M trainable parameters, compared to 23.1 M in aconventional generator with the same number of layers and feature maps.

Introducing spatial noise affects only the stochastic aspects of theoutput data, leaving the overall composition and high-level attributessuch as identity intact. Separate noise inputs to the synthesis neuralnetwork 140 enables the application of stochastic variation to differentsubsets of layers. Applying a spatial noise input to a particular layerof the synthesis neural network 140 leads to stochastic variation at ascale that matches the scale of the particular layer.

The effect of noise appears tightly localized in the synthesis neuralnetwork 140. At any point in the synthesis neural network 140, there ispressure to introduce new content as soon as possible, and the easiestway for the synthesis neural network 140 to create stochastic variationis to rely on the spatial noise inputs. A fresh set of spatial noise isavailable for each layer in the synthesis neural network 140, and thusthere is no incentive to generate the stochastic effects from earlieractivations, leading to a localized effect. Therefore, the noise affectsonly inconsequential stochastic variation (differently combed hair,beard, etc.). In contrast, changes to the style signals have globaleffects (changing pose, identity, etc.).

In the synthesis neural network 140, when the output data is an image,the style signals affect the entire image because complete feature mapsare scaled and biased with the same values. Therefore, global effectssuch as pose, lighting, or background style can be controlledcoherently. Meanwhile, the spatial noise is added independently to eachpixel and is thus ideally suited for controlling stochastic variation.If the synthesis neural network 140 tried to control, e.g., pose usingthe noise, that would lead to spatially inconsistent decisions thatwould be penalized during training. Thus, the synthesis neural network140 learns to use the global and local channels appropriately, withoutexplicit guidance.

FIG. 2C illustrates a flowchart of a method 250 for applying spatialnoise using the style-based generator system 100, in accordance with anembodiment. The method 250 may be performed by a program, customcircuitry, or by a combination of custom circuitry and a program. Forexample, the method 250 may be executed by a GPU (graphics processingunit), CPU (central processing unit), or any processor capable ofperforming the operations of the style-based generator system 100.Furthermore, persons of ordinary skill in the art will understand thatany system that performs method 250 is within the scope and spirit ofembodiments of the present invention.

At step 255, a first set of spatial noise is applied at a first layer ofthe synthesis neural network 140 to generate the first intermediate datacomprising content corresponding to source data that is modified basedon the first set of spatial noise. In an embodiment, the source data isthe first intermediate data and the first layer is a layer including themodule 220 and/or the convolution layer 225. At step 258, the modifiedfirst intermediate data is processed by the processing layer(s) 225 toproduce the second intermediate data. At step 260, a second set ofspatial noise is applied at a second layer of the synthesis neuralnetwork 140 to generate second intermediate data comprising contentcorresponding to the first intermediate data that is modified based onthe second set of spatial noise. In an embodiment, the firstintermediate data is modified by at least the module 220 to produce thesecond intermediate data. At step 265, the second intermediate data isprocessed to produce output data including content corresponding to thesecond intermediate data. In an embodiment, the second intermediate datais processed by another module 220 and the block 230 to produce theoutput data.

Noise may be injected into the layers of the synthesis neural network140 to cause synthesis of stochastic variations at a scale correspondingto the layer. Importantly, the noise should be injected during bothtraining and generation. Additionally, during generation, the strengthof the noise may be modified to further control the “look” of the outputdata. Providing style signals instead of directly inputting the latentcode into the synthesis neural network 140 in combination with noiseinjected directly into the synthesis neural network 140, leads toautomatic, unsupervised separation of high-level attributes (e.g., pose,identity) from stochastic variation (e.g., freckles, hair) in thegenerated images, and enables intuitive scale-specific mixing andinterpolation operations.

In particular, the style signals directly adjust the strength of imageattributes at different scales in the synthesis neural network 140.During generation, the style signals can be used to modify selectedimage attributes. Additionally, during training, the mapping neuralnetwork 110 may be configured to perform style mixing regularization toimprove localization of the styles.

The mapping neural network 110 embeds the input latent code into theintermediate latent space, which has a profound effect on how thefactors of variation are represented in the synthesis neural network140. The input latent space follows the probability density of thetraining data, and this likely leads to some degree of unavoidableentanglement. The intermediate latent space is free from thatrestriction and is therefore allowed to be disentangled. Compared to aconventional generator architecture, the style-based generator system100 admits a more linear, less entangled representation of differentfactors of variation. In an embodiment, replacing a conventionalgenerator with the style-based generator may not require modifying anyother component of the training framework (loss function, discriminator,optimization method, or the like).

The style-based generative neural network 100 may be trained using e.g.the GAN (generative adversarial networks), VAE (variational autoencoder)framework, flow-based framework, or the like. FIG. 2D illustrates ablock diagram of the GAN 270 training framework, in accordance with anembodiment. The GAN 270 may be implemented by a program, customcircuitry, or by a combination of custom circuitry and a program. Forexample, the GAN 270 may be implemented using a GPU, CPU, or anyprocessor capable of performing the operations described herein.Furthermore, persons of ordinary skill in the art will understand thatany system that performs the operations of the GAN 270 is within thescope and spirit of embodiments of the present invention.

The GAN 270 includes a generator, such as the style-based generatorsystem 100, a discriminator (neural network) 275, and a training lossunit 280. The topologies of both the generator 110 and discriminator 275may be modified during training. The GAN 270 may operate in anunsupervised setting or in a conditional setting. The style-basedgenerator system 100 receives input data (e.g., at least one latent codeand/or noise inputs) and produces output data. Depending on the task,the output data may be an image, audio, video, or other types of data(e.g., configuration setting). The discriminator 275 is an adaptive lossfunction that is used during training of the style-based generatorsystem 100. The style-based generator system 100 and discriminator 275are trained simultaneously using a training dataset that includesexample output data that the output data produced by the style-basedgenerator system 100 should be consistent with. The style-basedgenerator system 100 generates output data in response to the input dataand the discriminator 275 determines if the output data appears similarto the example output data included in the training data. Based on thedetermination, parameters of the discriminator 275 and/or thestyle-based generative neural network 100 are adjusted.

In the unsupervised setting, the discriminator 275 outputs a continuousvalue indicating how closely the output data matches the example outputdata. For example, in an embodiment, the discriminator 275 outputs afirst training stimulus (e.g., high value) when the output data isdetermined to match the example output data and a second trainingstimulus (e.g., low value) when the output data is determined to notmatch the example output data. The training loss unit 280 adjustsparameters (weights) of the GAN 270 based on the output of thediscriminator 275. When the style-based generator system 100 is trainedfor a specific task, such as generating images of human faces, thediscriminator outputs a high value when the output data is an image of ahuman face. The output data generated by the style-based generatorsystem 100 is not required to be identical to the example output datafor the discriminator 275 to determine the output data matches theexample output data. In the context of the following description, thediscriminator 275 determines that the output data matches the exampleoutput data when the output data is perceptually similar to any of theexample output data.

In the conditional setting, the input of the style-based generativeneural network 100 may include other data, such as an image, aclassification label, segmentation contours, and other (additional)types of data (distribution, audio, etc.). The additional data may bespecified in addition to the random latent code, or the additional datamay replace the random latent code altogether. The training dataset mayinclude input/output data pairs, and the task of the discriminator 275may be to determine if the output of the style-based generative neuralnetwork 100 appears consistent with the input, based on the exampleinput/output pairs that the discriminator 275 has seen in the trainingdata.

In an embodiment, the style-based generative neural network 100 may betrained using a progressive growing technique. In one embodiment, themapping neural network 110 and/or the synthesis neural network 140 areinitially implemented as a generator neural network portion of a GAN andtrained using a progressive growing technique, as described in Karras etal., “Progressive Growing of GANs for Improved Quality, Stability, andVariation,” Sixth International Conference on Learning Representations(ICLR), (Apr. 30, 2018), which is herein incorporated by reference inits entirety.

Parallel Processing Architecture

FIG. 3 illustrates a parallel processing unit (PPU) 300, in accordancewith an embodiment. In an embodiment, the PPU 300 is a multi-threadedprocessor that is implemented on one or more integrated circuit devices.The PPU 300 is a latency hiding architecture designed to process manythreads in parallel. A thread (i.e., a thread of execution) is aninstantiation of a set of instructions configured to be executed by thePPU 300. In an embodiment, the PPU 300 is a graphics processing unit(GPU) configured to implement a graphics rendering pipeline forprocessing three-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device such asa liquid crystal display (LCD) device. In another embodiment, the PPU300 is configured to implement the neural network system 100. In otherembodiments, the PPU 300 may be utilized for performing general-purposecomputations. While one exemplary parallel processor is provided hereinfor illustrative purposes, it should be strongly noted that suchprocessor is set forth for illustrative purposes only, and that anyprocessor may be employed to supplement and/or substitute for the same.

One or more PPUs 300 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, cloud computing, and machinelearning applications. The PPU 300 may be configured to acceleratenumerous deep learning systems and applications for autonomous vehicles,simulation, computational graphics such as ray or path tracing, deeplearning, high-accuracy speech, image, and text recognition systems,intelligent video analytics, molecular simulations, drug discovery,disease diagnosis, weather forecasting, big data analytics, astronomy,molecular dynamics simulation, financial modeling, robotics, factoryautomation, real-time language translation, online search optimizations,and personalized user recommendations, and the like.

As shown in FIG. 3 , the PPU 300 includes an Input/Output (I/O) unit305, a front end unit 315, a scheduler unit 320, a work distributionunit 325, a hub 330, a crossbar (Xbar) 370, one or more generalprocessing clusters (GPCs) 350, and one or more memory partition units380. The PPU 300 may be connected to a host processor or other PPUs 300via one or more high-speed NVLink 310 interconnect. The PPU 300 may beconnected to a host processor or other peripheral devices via aninterconnect 302. The PPU 300 may also be connected to a local memory304 comprising a number of memory devices. In an embodiment, the localmemory may comprise a number of dynamic random access memory (DRAM)devices. The DRAM devices may be configured as a high-bandwidth memory(HBM) subsystem, with multiple DRAM dies stacked within each device.

The NVLink 310 interconnect enables systems to scale and include one ormore PPUs 300 combined with one or more CPUs, supports cache coherencebetween the PPUs 300 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 310 through the hub 330 to/from otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 310 is described in more detail in conjunction with FIG. 5B.

The I/O unit 305 is configured to transmit and receive communications(e.g., commands, data, etc.) from a host processor (not shown) over theinterconnect 302. The I/O unit 305 may communicate with the hostprocessor directly via the interconnect 302 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 305 may communicate with one or more other processors, such as oneor more the PPUs 300 via the interconnect 302. In an embodiment, the I/Ounit 305 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 302 isa PCIe bus. In alternative embodiments, the I/O unit 305 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 305 decodes packets received via the interconnect 302. Inan embodiment, the packets represent commands configured to cause thePPU 300 to perform various operations. The I/O unit 305 transmits thedecoded commands to various other units of the PPU 300 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 315. Other commands may be transmitted to the hub 330 or otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 305 is configured to route communicationsbetween and among the various logical units of the PPU 300.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 300 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (e.g., read/write) by both the host processor and the PPU300. For example, the I/O unit 305 may be configured to access thebuffer in a system memory connected to the interconnect 302 via memoryrequests transmitted over the interconnect 302. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 300.The front end unit 315 receives pointers to one or more command streams.The front end unit 315 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU300.

The front end unit 315 is coupled to a scheduler unit 320 thatconfigures the various GPCs 350 to process tasks defined by the one ormore streams. The scheduler unit 320 is configured to track stateinformation related to the various tasks managed by the scheduler unit320. The state may indicate which GPC 350 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 320 manages the execution of aplurality of tasks on the one or more GPCs 350.

The scheduler unit 320 is coupled to a work distribution unit 325 thatis configured to dispatch tasks for execution on the GPCs 350. The workdistribution unit 325 may track a number of scheduled tasks receivedfrom the scheduler unit 320. In an embodiment, the work distributionunit 325 manages a pending task pool and an active task pool for each ofthe GPCs 350. The pending task pool may comprise a number of slots(e.g., 32 slots) that contain tasks assigned to be processed by aparticular GPC 350. The active task pool may comprise a number of slots(e.g., 4 slots) for tasks that are actively being processed by the GPCs350. As a GPC 350 finishes the execution of a task, that task is evictedfrom the active task pool for the GPC 350 and one of the other tasksfrom the pending task pool is selected and scheduled for execution onthe GPC 350. If an active task has been idle on the GPC 350, such aswhile waiting for a data dependency to be resolved, then the active taskmay be evicted from the GPC 350 and returned to the pending task poolwhile another task in the pending task pool is selected and scheduledfor execution on the GPC 350.

The work distribution unit 325 communicates with the one or more GPCs350 via XBar 370. The XBar 370 is an interconnect network that couplesmany of the units of the PPU 300 to other units of the PPU 300. Forexample, the XBar 370 may be configured to couple the work distributionunit 325 to a particular GPC 350. Although not shown explicitly, one ormore other units of the PPU 300 may also be connected to the XBar 370via the hub 330.

The tasks are managed by the scheduler unit 320 and dispatched to a GPC350 by the work distribution unit 325. The GPC 350 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 350, routed to a different GPC 350 via theXBar 370, or stored in the memory 304. The results can be written to thememory 304 via the memory partition units 380, which implement a memoryinterface for reading and writing data to/from the memory 304. Theresults can be transmitted to another PPU 300 or CPU via the NVLink 310.In an embodiment, the PPU 300 includes a number U of memory partitionunits 380 that is equal to the number of separate and distinct memorydevices of the memory 304 coupled to the PPU 300. A memory partitionunit 380 will be described in more detail below in conjunction with FIG.4B.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 300. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 300 and thePPU 300 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (e.g., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 300. The driverkernel outputs tasks to one or more streams being processed by the PPU300. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. Threads andcooperating threads are described in more detail in conjunction withFIG. 5A.

FIG. 4A illustrates a GPC 350 of the PPU 300 of FIG. 3 , in accordancewith an embodiment. As shown in FIG. 4A, each GPC 350 includes a numberof hardware units for processing tasks. In an embodiment, each GPC 350includes a pipeline manager 410, a pre-raster operations unit (PROP)415, a raster engine 425, a work distribution crossbar (WDX) 480, amemory management unit (MMU) 490, and one or more Data ProcessingClusters (DPCs) 420. It will be appreciated that the GPC 350 of FIG. 4Amay include other hardware units in lieu of or in addition to the unitsshown in FIG. 4A.

In an embodiment, the operation of the GPC 350 is controlled by thepipeline manager 410. The pipeline manager 410 manages the configurationof the one or more DPCs 420 for processing tasks allocated to the GPC350. In an embodiment, the pipeline manager 410 may configure at leastone of the one or more DPCs 420 to implement at least a portion of agraphics rendering pipeline. For example, a DPC 420 may be configured toexecute a vertex shader program on the programmable streamingmultiprocessor (SM) 440. The pipeline manager 410 may also be configuredto route packets received from the work distribution unit 325 to theappropriate logical units within the GPC 350. For example, some packetsmay be routed to fixed function hardware units in the PROP 415 and/orraster engine 425 while other packets may be routed to the DPCs 420 forprocessing by the primitive engine 435 or the SM 440. In an embodiment,the pipeline manager 410 may configure at least one of the one or moreDPCs 420 to implement a neural network model and/or a computingpipeline.

The PROP unit 415 is configured to route data generated by the rasterengine 425 and the DPCs 420 to a Raster Operations (ROP) unit, describedin more detail in conjunction with FIG. 4B. The PROP unit 415 may alsobe configured to perform optimizations for color blending, organizepixel data, perform address translations, and the like.

The raster engine 425 includes a number of fixed function hardware unitsconfigured to perform various raster operations. In an embodiment, theraster engine 425 includes a setup engine, a coarse raster engine, aculling engine, a clipping engine, a fine raster engine, and a tilecoalescing engine. The setup engine receives transformed vertices andgenerates plane equations associated with the geometric primitivedefined by the vertices. The plane equations are transmitted to thecoarse raster engine to generate coverage information (e.g., an x,ycoverage mask for a tile) for the primitive. The output of the coarseraster engine is transmitted to the culling engine where fragmentsassociated with the primitive that fail a z-test are culled, andtransmitted to a clipping engine where fragments lying outside a viewingfrustum are clipped. Those fragments that survive clipping and cullingmay be passed to the fine raster engine to generate attributes for thepixel fragments based on the plane equations generated by the setupengine. The output of the raster engine 425 comprises fragments to beprocessed, for example, by a fragment shader implemented within a DPC420.

Each DPC 420 included in the GPC 350 includes an M-Pipe Controller (MPC)430, a primitive engine 435, and one or more SMs 440. The MPC 430controls the operation of the DPC 420, routing packets received from thepipeline manager 410 to the appropriate units in the DPC 420. Forexample, packets associated with a vertex may be routed to the primitiveengine 435, which is configured to fetch vertex attributes associatedwith the vertex from the memory 304. In contrast, packets associatedwith a shader program may be transmitted to the SM 440.

The SM 440 comprises a programmable streaming processor that isconfigured to process tasks represented by a number of threads. Each SM440 is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently. Inan embodiment, the SM 440 implements a SIMD (Single-Instruction,Multiple-Data) architecture where each thread in a group of threads(e.g., a warp) is configured to process a different set of data based onthe same set of instructions. All threads in the group of threadsexecute the same instructions. In another embodiment, the SM 440implements a SIMT (Single-Instruction, Multiple Thread) architecturewhere each thread in a group of threads is configured to process adifferent set of data based on the same set of instructions, but whereindividual threads in the group of threads are allowed to diverge duringexecution. In an embodiment, a program counter, call stack, andexecution state is maintained for each warp, enabling concurrencybetween warps and serial execution within warps when threads within thewarp diverge. In another embodiment, a program counter, call stack, andexecution state is maintained for each individual thread, enabling equalconcurrency between all threads, within and between warps. Whenexecution state is maintained for each individual thread, threadsexecuting the same instructions may be converged and executed inparallel for maximum efficiency. The SM 440 will be described in moredetail below in conjunction with FIG. 5A.

The MMU 490 provides an interface between the GPC 350 and the memorypartition unit 380. The MMU 490 may provide translation of virtualaddresses into physical addresses, memory protection, and arbitration ofmemory requests. In an embodiment, the MMU 490 provides one or moretranslation lookaside buffers (TLBs) for performing translation ofvirtual addresses into physical addresses in the memory 304.

FIG. 4B illustrates a memory partition unit 380 of the PPU 300 of FIG. 3, in accordance with an embodiment. As shown in FIG. 4B, the memorypartition unit 380 includes a Raster Operations (ROP) unit 450, a leveltwo (L2) cache 460, and a memory interface 470. The memory interface 470is coupled to the memory 304. Memory interface 470 may implement 32, 64,128, 1024-bit data buses, or the like, for high-speed data transfer. Inan embodiment, the PPU 300 incorporates U memory interfaces 470, onememory interface 470 per pair of memory partition units 380, where eachpair of memory partition units 380 is connected to a correspondingmemory device of the memory 304. For example, PPU 300 may be connectedto up to Y memory devices, such as high bandwidth memory stacks orgraphics double-data-rate, version 5, synchronous dynamic random accessmemory, or other types of persistent storage.

In an embodiment, the memory interface 470 implements an HBM2 memoryinterface and Y equals half U. In an embodiment, the HBM2 memory stacksare located on the same physical package as the PPU 300, providingsubstantial power and area savings compared with conventional GDDR5SDRAM systems. In an embodiment, each HBM2 stack includes four memorydies and Y equals 4, with HBM2 stack including two 128-bit channels perdie for a total of 8 channels and a data bus width of 1024 bits.

In an embodiment, the memory 304 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 300 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 300 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 380 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU300 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 300 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 300 that is accessing the pages morefrequently. In an embodiment, the NVLink 310 supports addresstranslation services allowing the PPU 300 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 300.

In an embodiment, copy engines transfer data between multiple PPUs 300or between PPUs 300 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 380 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (e.g.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 304 or other system memory may be fetched by thememory partition unit 380 and stored in the L2 cache 460, which islocated on-chip and is shared between the various GPCs 350. As shown,each memory partition unit 380 includes a portion of the L2 cache 460associated with a corresponding memory 304. Lower level caches may thenbe implemented in various units within the GPCs 350. For example, eachof the SMs 440 may implement a level one (L1) cache. The L1 cache isprivate memory that is dedicated to a particular SM 440. Data from theL2 cache 460 may be fetched and stored in each of the L1 caches forprocessing in the functional units of the SMs 440. The L2 cache 460 iscoupled to the memory interface 470 and the XBar 370.

The ROP unit 450 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and the like. The ROPunit 450 also implements depth testing in conjunction with the rasterengine 425, receiving a depth for a sample location associated with apixel fragment from the culling engine of the raster engine 425. Thedepth is tested against a corresponding depth in a depth buffer for asample location associated with the fragment. If the fragment passes thedepth test for the sample location, then the ROP unit 450 updates thedepth buffer and transmits a result of the depth test to the rasterengine 425. It will be appreciated that the number of memory partitionunits 380 may be different than the number of GPCs 350 and, therefore,each ROP unit 450 may be coupled to each of the GPCs 350. The ROP unit450 tracks packets received from the different GPCs 350 and determineswhich GPC 350 that a result generated by the ROP unit 450 is routed tothrough the Xbar 370. Although the ROP unit 450 is included within thememory partition unit 380 in FIG. 4B, in other embodiment, the ROP unit450 may be outside of the memory partition unit 380. For example, theROP unit 450 may reside in the GPC 350 or another unit.

FIG. 5A illustrates the streaming multi-processor 440 of FIG. 4A, inaccordance with an embodiment. As shown in FIG. 5A, the SM 440 includesan instruction cache 505, one or more scheduler units 510, a registerfile 520, one or more processing cores 550, one or more special functionunits (SFUs) 552, one or more load/store units (LSUs) 554, aninterconnect network 580, a shared memory/L1 cache 570.

As described above, the work distribution unit 325 dispatches tasks forexecution on the GPCs 350 of the PPU 300. The tasks are allocated to aparticular DPC 420 within a GPC 350 and, if the task is associated witha shader program, the task may be allocated to an SM 440. The schedulerunit 510 receives the tasks from the work distribution unit 325 andmanages instruction scheduling for one or more thread blocks assigned tothe SM 440. The scheduler unit 510 schedules thread blocks for executionas warps of parallel threads, where each thread block is allocated atleast one warp. In an embodiment, each warp executes 32 threads. Thescheduler unit 510 may manage a plurality of different thread blocks,allocating the warps to the different thread blocks and then dispatchinginstructions from the plurality of different cooperative groups to thevarious functional units (e.g., cores 550, SFUs 552, and LSUs 554)during each clock cycle.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (e.g., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (e.g., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

A dispatch unit 515 is configured to transmit instructions to one ormore of the functional units. In the embodiment, the scheduler unit 510includes two dispatch units 515 that enable two different instructionsfrom the same warp to be dispatched during each clock cycle. Inalternative embodiments, each scheduler unit 510 may include a singledispatch unit 515 or additional dispatch units 515.

Each SM 440 includes a register file 520 that provides a set ofregisters for the functional units of the SM 440. In an embodiment, theregister file 520 is divided between each of the functional units suchthat each functional unit is allocated a dedicated portion of theregister file 520. In another embodiment, the register file 520 isdivided between the different warps being executed by the SM 440. Theregister file 520 provides temporary storage for operands connected tothe data paths of the functional units.

Each SM 440 comprises L processing cores 550. In an embodiment, the SM440 includes a large number (e.g., 128, etc.) of distinct processingcores 550. Each core 550 may include a fully-pipelined,single-precision, double-precision, and/or mixed precision processingunit that includes a floating point arithmetic logic unit and an integerarithmetic logic unit. In an embodiment, the floating point arithmeticlogic units implement the IEEE 754-2008 standard for floating pointarithmetic. In an embodiment, the cores 550 include 64 single-precision(32-bit) floating point cores, 64 integer cores, 32 double-precision(64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations, and, in anembodiment, one or more tensor cores are included in the cores 550. Inparticular, the tensor cores are configured to perform deep learningmatrix arithmetic, such as convolution operations for neural networktraining and inferencing. In an embodiment, each tensor core operates ona 4×4 matrix and performs a matrix multiply and accumulate operationD=A×B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each SM 440 also comprises M SFUs 552 that perform special functions(e.g., attribute evaluation, reciprocal square root, and the like). Inan embodiment, the SFUs 552 may include a tree traversal unit configuredto traverse a hierarchical tree data structure. In an embodiment, theSFUs 552 may include texture unit configured to perform texture mapfiltering operations. In an embodiment, the texture units are configuredto load texture maps (e.g., a 2D array of texels) from the memory 304and sample the texture maps to produce sampled texture values for use inshader programs executed by the SM 440. In an embodiment, the texturemaps are stored in the shared memory/L1 cache 470. The texture unitsimplement texture operations such as filtering operations using mip-maps(e.g., texture maps of varying levels of detail). In an embodiment, eachSM 340 includes two texture units.

Each SM 440 also comprises N LSUs 554 that implement load and storeoperations between the shared memory/L1 cache 570 and the register file520. Each SM 440 includes an interconnect network 580 that connects eachof the functional units to the register file 520 and the LSU 554 to theregister file 520, shared memory/L1 cache 570. In an embodiment, theinterconnect network 580 is a crossbar that can be configured to connectany of the functional units to any of the registers in the register file520 and connect the LSUs 554 to the register file and memory locationsin shared memory/L1 cache 570.

The shared memory/L1 cache 570 is an array of on-chip memory that allowsfor data storage and communication between the SM 440 and the primitiveengine 435 and between threads in the SM 440. In an embodiment, theshared memory/L1 cache 570 comprises 128 KB of storage capacity and isin the path from the SM 440 to the memory partition unit 380. The sharedmemory/L1 cache 570 can be used to cache reads and writes. One or moreof the shared memory/L1 cache 570, L2 cache 460, and memory 304 arebacking stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory/L1 cache 570enables the shared memory/L1 cache 570 to function as a high-throughputconduit for streaming data while simultaneously providing high-bandwidthand low-latency access to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, the fixed function graphics processing units shown in FIG.3 , are bypassed, creating a much simpler programming model. In thegeneral purpose parallel computation configuration, the workdistribution unit 325 assigns and distributes blocks of threads directlyto the DPCs 420. The threads in a block execute the same program, usinga unique thread ID in the calculation to ensure each thread generatesunique results, using the SM 440 to execute the program and performcalculations, shared memory/L1 cache 570 to communicate between threads,and the LSU 554 to read and write global memory through the sharedmemory/L1 cache 570 and the memory partition unit 380. When configuredfor general purpose parallel computation, the SM 440 can also writecommands that the scheduler unit 320 can use to launch new work on theDPCs 420.

The PPU 300 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 300 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 300 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 300, the memory 304, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 300 may be included on a graphics card thatincludes one or more memory devices. The graphics card may be configuredto interface with a PCIe slot on a motherboard of a desktop computer. Inyet another embodiment, the PPU 300 may be an integrated graphicsprocessing unit (iGPU) or parallel processor included in the chipset ofthe motherboard.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 5B is a conceptual diagram of a processing system 500 implementedusing the PPU 300 of FIG. 3 , in accordance with an embodiment. Theexemplary system 565 may be configured to implement one or more of themethods 150, 250, 650, and 675 shown in FIGS. 1C, 2C, 6C, and 6D,respectively. The processing system 500 includes a CPU 530, switch 510,and multiple PPUs 300, and respective memories 304. The PPUs 330 mayeach include, and/or be configured to perform functions of, one or moreprocessing cores and/or components thereof, such as Tensor Cores (TCs),Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs), VisionProcessing Units (VPUs), Graphics Processing Clusters (GPCs), TextureProcessing Clusters (TPCs), Streaming Multiprocessors (SMs), TreeTraversal Units (TTUs), Artificial Intelligence Accelerators (AIAs),Deep Learning Accelerators (DLAs), Arithmetic-Logic Units (ALUs),Application-Specific Integrated Circuits (ASICs), Floating Point Units(FPUs), input/output (I/O) elements, peripheral component interconnect(PCI) or peripheral component interconnect express (PCIe) elements,and/or the like.

The NVLink 310 provides high-speed communication links between each ofthe PPUs 300. Although a particular number of NVLink 310 andinterconnect 302 connections are illustrated in FIG. 5B, the number ofconnections to each PPU 300 and the CPU 530 may vary. The switch 510interfaces between the interconnect 302 and the CPU 530. The PPUs 300,memories 304, and NVLinks 310 may be situated on a single semiconductorplatform to form a parallel processing module 525. In an embodiment, theswitch 510 supports two or more protocols to interface between variousdifferent connections and/or links.

In another embodiment (not shown), the NVLink 310 provides one or morehigh-speed communication links between each of the PPUs 300 and the CPU530 and the switch 510 interfaces between the interconnect 302 and eachof the PPUs 300. The PPUs 300, memories 304, and interconnect 302 may besituated on a single semiconductor platform to form a parallelprocessing module 525. In yet another embodiment (not shown), theinterconnect 302 provides one or more communication links between eachof the PPUs 300 and the CPU 530 and the switch 510 interfaces betweeneach of the PPUs 300 using the NVLink 310 to provide one or morehigh-speed communication links between the PPUs 300. In anotherembodiment (not shown), the NVLink 310 provides one or more high-speedcommunication links between the PPUs 300 and the CPU 530 through theswitch 510. In yet another embodiment (not shown), the interconnect 302provides one or more communication links between each of the PPUs 300directly. One or more of the NVLink 310 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink310.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 525 may be implemented as a circuit board substrateand each of the PPUs 300 and/or memories 304 may be packaged devices. Inan embodiment, the CPU 530, switch 510, and the parallel processingmodule 525 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 310 is 20 to 25Gigabits/second and each PPU 300 includes six NVLink 310 interfaces (asshown in FIG. 5B, five NVLink 310 interfaces are included for each PPU300). Each NVLink 310 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 300Gigabytes/second. The NVLinks 310 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 5B, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 530 also includes one or more NVLink 310interfaces.

In an embodiment, the NVLink 310 allows direct load/store/atomic accessfrom the CPU 530 to each PPU's 300 memory 304. In an embodiment, theNVLink 310 supports coherency operations, allowing data read from thememories 304 to be stored in the cache hierarchy of the CPU 530,reducing cache access latency for the CPU 530. In an embodiment, theNVLink 310 includes support for Address Translation Services (ATS),allowing the PPU 300 to directly access page tables within the CPU 530.One or more of the NVLinks 310 may also be configured to operate in alow-power mode.

FIG. 5C illustrates an exemplary system 565 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 565 may be configured toimplement one or more of the methods 150, 250, 650, and 675 shown inFIGS. 1C, 2C, 6C, and 6D, respectively.

As shown, a system 565 is provided including at least one centralprocessing unit 530 that is connected to a communication bus 575. Thecommunication bus 575 may directly or indirectly couple one or more ofthe following devices: main memory 540, network interface 535, CPU(s)530, display device(s) 545, input device(s) 560, switch 510, andparallel processing system 525. The communication bus 575 may beimplemented using any suitable protocol and may represent one or morelinks or busses, such as an address bus, a data bus, a control bus, or acombination thereof. The communication bus 575 may include one or morebus or link types, such as an industry standard architecture (ISA) bus,an extended industry standard architecture (EISA) bus, a videoelectronics standards association (VESA) bus, a peripheral componentinterconnect (PCI) bus, a peripheral component interconnect express(PCIe) bus, HyperTransport, and/or another type of bus or link. In someembodiments, there are direct connections between components. As anexample, the CPU(s) 530 may be directly connected to the main memory540. Further, the CPU(s) 530 may be directly connected to the parallelprocessing system 525. Where there is direct, or point-to-pointconnection between components, the communication bus 575 may include aPCIe link to carry out the connection. In these examples, a PCI bus neednot be included in the system 565.

Although the various blocks of FIG. 5C are shown as connected via thecommunication bus 575 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component, such as display device(s) 545, may be consideredan I/O component, such as input device(s) 560 (e.g., if the display is atouch screen). As another example, the CPU(s) 530 and/or parallelprocessing system 525 may include memory (e.g., the main memory 540 maybe representative of a storage device in addition to the parallelprocessing system 525, the CPUs 530, and/or other components). In otherwords, the computing device of FIG. 5C is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.5C.

The system 565 also includes a main memory 540. Control logic (software)and data are stored in the main memory 540 which may take the form of avariety of computer-readable media. The computer-readable media may beany available media that may be accessed by the system 565. Thecomputer-readable media may include both volatile and nonvolatile media,and removable and non-removable media. By way of example, and notlimitation, the computer-readable media may comprise computer-storagemedia and communication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the main memory 540 may store computer-readableinstructions (e.g., that represent a program(s) and/or a programelement(s), such as an operating system. Computer-storage media mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which maybe used to store the desired information and which may be accessed bysystem 565. As used herein, computer storage media does not comprisesignals per se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

Computer programs, when executed, enable the system 565 to performvarious functions. The CPU(s) 530 may be configured to execute at leastsome of the computer-readable instructions to control one or morecomponents of the system 565 to perform one or more of the methodsand/or processes described herein. The CPU(s) 530 may each include oneor more cores (e.g., one, two, four, eight, twenty-eight, seventy-two,etc.) that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 530 may include any type of processor, andmay include different types of processors depending on the type ofsystem 565 implemented (e.g., processors with fewer cores for mobiledevices and processors with more cores for servers). For example,depending on the type of system 565, the processor may be an AdvancedRISC Machines (ARM) processor implemented using Reduced Instruction SetComputing (RISC) or an x86 processor implemented using ComplexInstruction Set Computing (CISC). The system 565 may include one or moreCPUs 530 in addition to one or more microprocessors or supplementaryco-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 530, the parallelprocessing module 525 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thesystem 565 to perform one or more of the methods and/or processesdescribed herein. The parallel processing module 525 may be used by thesystem 565 to render graphics (e.g., 3D graphics) or perform generalpurpose computations. For example, the parallel processing module 525may be used for General-Purpose computing on GPUs (GPGPU). Inembodiments, the CPU(s) 530 and/or the parallel processing module 525may discretely or jointly perform any combination of the methods,processes and/or portions thereof.

The system 565 also includes input device(s) 560, the parallelprocessing system 525, and display device(s) 545. The display device(s)545 may include a display (e.g., a monitor, a touch screen, a televisionscreen, a heads-up-display (HUD), other display types, or a combinationthereof), speakers, and/or other presentation components. The displaydevice(s) 545 may receive data from other components (e.g., the parallelprocessing system 525, the CPU(s) 530, etc.), and output the data (e.g.,as an image, video, sound, etc.).

The network interface 535 may enable the system 565 to be logicallycoupled to other devices including the input devices 560, the displaydevice(s) 545, and/or other components, some of which may be built in to(e.g., integrated in) the system 565. Illustrative input devices 560include a microphone, mouse, keyboard, joystick, game pad, gamecontroller, satellite dish, scanner, printer, wireless device, etc. Theinput devices 560 may provide a natural user interface (NUI) thatprocesses air gestures, voice, or other physiological inputs generatedby a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described in more detail below) associated with adisplay of the system 565. The system 565 may be include depth cameras,such as stereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the system 565 may includeinput devices 560 such as accelerometers or gyroscopes (e.g., as part ofan inertia measurement unit (IMU)) that enable detection of motion. Insome examples, the output of the accelerometers or gyroscopes may beused by the system 565 to render immersive augmented reality or virtualreality.

Further, the system 565 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 535 forcommunication purposes. The system 565 may be included within adistributed network and/or cloud computing environment.

The network interface 535 may include one or more receivers,transmitters, and/or transceivers that enable the system 565 tocommunicate with other computing devices via an electronic communicationnetwork, included wired and/or wireless communications. The networkinterface 535 may include components and functionality to enablecommunication over any of a number of different networks, such aswireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee,etc.), wired networks (e.g., communicating over Ethernet or InfiniBand),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The system 565 may also include a secondary storage (not shown). Thesecondary storage 610 includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner. The system 565 may also include a hard-wired powersupply, a battery power supply, or a combination thereof (not shown).The power supply may provide power to the system 565 to enable thecomponents of the system 565 to operate.

Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 565. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of theprocessing system 500 of FIG. 5B and/or exemplary system 565 of FIG.5C—e.g., each device may include similar components, features, and/orfunctionality of the processing system 500 and/or exemplary system 565.

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more WANs, one or more LANs, one or morepublic networks such as the Internet and/or a public switched telephonenetwork (PSTN), and/or one or more private networks. Where the networkincludes a wireless telecommunications network, components such as abase station, a communications tower, or even access points (as well asother components) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example processing system 500 of FIG.5B and/or exemplary system 565 of FIG. 5C. By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 300have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit to get smarter and more efficient at identifying basic objects,occluded objects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected nodes (e.g., perceptrons, Boltzmann machines, radial basisfunctions, convolutional layers, etc.) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DNN model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 300. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 300 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

Furthermore, images generated applying one or more of the techniquesdisclosed herein may be used to train, test, or certify DNNs used torecognize objects and environments in the real world. Such images mayinclude scenes of roadways, factories, buildings, urban settings, ruralsettings, humans, animals, and any other physical object or real-worldsetting. Such images may be used to train, test, or certify DNNs thatare employed in machines or robots to manipulate, handle, or modifyphysical objects in the real world. Furthermore, such images may be usedto train, test, or certify DNNs that are employed in autonomous vehiclesto navigate and move the vehicles through the real world. Additionally,images generated applying one or more of the techniques disclosed hereinmay be used to convey information to users of such machines, robots, andvehicles.

FIG. 5D illustrates components of an exemplary system 555 that can beused to train and utilize machine learning, in accordance with at leastone embodiment. As will be discussed, various components can be providedby various combinations of computing devices and resources, or a singlecomputing system, which may be under control of a single entity ormultiple entities. Further, aspects may be triggered, initiated, orrequested by different entities. In at least one embodiment training ofa neural network might be instructed by a provider associated withprovider environment 506, while in at least one embodiment trainingmight be requested by a customer or other user having access to aprovider environment through a client device 502 or other such resource.In at least one embodiment, training data (or data to be analyzed by atrained neural network) can be provided by a provider, a user, or athird party content provider 524. In at least one embodiment, clientdevice 502 may be a vehicle or object that is to be navigated on behalfof a user, for example, which can submit requests and/or receiveinstructions that assist in navigation of a device.

In at least one embodiment, requests are able to be submitted across atleast one network 504 to be received by a provider environment 506. Inat least one embodiment, a client device 502 may be any appropriateelectronic and/or computing devices enabling a user to generate and sendsuch requests, such as, but not limited to, desktop computers, notebookcomputers, computer servers, smartphones, tablet computers, gamingconsoles (portable or otherwise), computer processors, computing logic,and set-top boxes. Network(s) 504 can include any appropriate networkfor transmitting a request or other such data, as may include theInternet, an intranet, a cellular network, a local area network (LAN), awide area network (WAN), a personal area network (PAN), an ad hocnetwork of direct wireless connections among peers, and so on.

In at least one embodiment, requests can be received at an interfacelayer 508, which can forward data to a training and inference manager532, in this example. The training and inference manager 532 can be asystem or service including hardware and software for managing requestsand service corresponding data or content, in at least one embodiment,the training and inference manager 532 can receive a request to train aneural network, and can provide data for a request to a training module512. In at least one embodiment, training module 512 can select anappropriate model or neural network to be used, if not specified by therequest, and can train a model using relevant training data. In at leastone embodiment, training data can be a batch of data stored in atraining data repository 514, received from client device 502, orobtained from a third party provider 524. In at least one embodiment,training module 512 can be responsible for training data. A neuralnetwork can be any appropriate network, such as a recurrent neuralnetwork (RNN) or convolutional neural network (CNN). Once a neuralnetwork is trained and successfully evaluated, a trained neural networkcan be stored in a model repository 516, for example, that may storedifferent models or networks for users, applications, or services, etc.In at least one embodiment, there may be multiple models for a singleapplication or entity, as may be utilized based on a number of differentfactors.

In at least one embodiment, at a subsequent point in time, a request maybe received from client device 502 (or another such device) for content(e.g., path determinations) or data that is at least partiallydetermined or impacted by a trained neural network. This request caninclude, for example, input data to be processed using a neural networkto obtain one or more inferences or other output values,classifications, or predictions, or, for at least one embodiment, inputdata can be received by interface layer 508 and directed to inferencemodule 518, although a different system or service can be used as well.In at least one embodiment, inference module 518 can obtain anappropriate trained network, such as a trained deep neural network (DNN)as discussed herein, from model repository 516 if not already storedlocally to inference module 518. Inference module 518 can provide dataas input to a trained network, which can then generate one or moreinferences as output. This may include, for example, a classification ofan instance of input data. In at least one embodiment, inferences canthen be transmitted to client device 502 for display or othercommunication to a user. In at least one embodiment, context data for auser may also be stored to a user context data repository 522, which mayinclude data about a user which may be useful as input to a network ingenerating inferences, or determining data to return to a user afterobtaining instances. In at least one embodiment, relevant data, whichmay include at least some of input or inference data, may also be storedto a local database 534 for processing future requests. In at least oneembodiment, a user can use account information or other information toaccess resources or functionality of a provider environment. In at leastone embodiment, if permitted and available, user data may also becollected and used to further train models, in order to provide moreaccurate inferences for future requests. In at least one embodiment,requests may be received through a user interface to a machine learningapplication 526 executing on client device 502, and results displayedthrough a same interface. A client device can include resources such asa processor 528 and memory 562 for generating a request and processingresults or a response, as well as at least one data storage element 552for storing data for machine learning application 526.

In at least one embodiment a processor 528 (or a processor of trainingmodule 512 or inference module 518) will be a central processing unit(CPU). As mentioned, however, resources in such environments can utilizeGPUs to process data for at least certain types of requests. Withthousands of cores, GPUs, such as PPU 300 are designed to handlesubstantial parallel workloads and, therefore, have become popular indeep learning for training neural networks and generating predictions.While use of GPUs for offline builds has enabled faster training oflarger and more complex models, generating predictions offline impliesthat either request-time input features cannot be used or predictionsmust be generated for all permutations of features and stored in alookup table to serve real-time requests. If a deep learning frameworksupports a CPU-mode and a model is small and simple enough to perform afeed-forward on a CPU with a reasonable latency, then a service on a CPUinstance could host a model. In this case, training can be done offlineon a GPU and inference done in real-time on a CPU. If a CPU approach isnot viable, then a service can run on a GPU instance. Because GPUs havedifferent performance and cost characteristics than CPUs, however,running a service that offloads a runtime algorithm to a GPU can requireit to be designed differently from a CPU based service.

In at least one embodiment, video data can be provided from clientdevice 502 for enhancement in provider environment 506. In at least oneembodiment, video data can be processed for enhancement on client device502. In at least one embodiment, video data may be streamed from a thirdparty content provider 524 and enhanced by third party content provider524, provider environment 506, or client device 502. In at least oneembodiment, video data can be provided from client device 502 for use astraining data in provider environment 506.

In at least one embodiment, supervised and/or unsupervised training canbe performed by the client device 502 and/or the provider environment506. In at least one embodiment, a set of training data 514 (e.g.,classified or labeled data) is provided as input to function as trainingdata. In an embodiment, the set of training data may be used in agenerative adversarial training configuration to train a generatorneural network.

In at least one embodiment, training data can include images of at leastone human subject, avatar, or character for which a neural network is tobe trained. In at least one embodiment, training data can includeinstances of at least one type of object for which a neural network isto be trained, as well as information that identifies that type ofobject. In at least one embodiment, training data might include a set ofimages that each includes a representation of a type of object, whereeach image also includes, or is associated with, a label, metadata,classification, or other piece of information identifying a type ofobject represented in a respective image. Various other types of datamay be used as training data as well, as may include text data, audiodata, video data, and so on. In at least one embodiment, training data514 is provided as training input to a training module 512. In at leastone embodiment, training module 512 can be a system or service thatincludes hardware and software, such as one or more computing devicesexecuting a training application, for training a neural network (orother model or algorithm, etc.). In at least one embodiment, trainingmodule 512 receives an instruction or request indicating a type of modelto be used for training, in at least one embodiment, a model can be anyappropriate statistical model, network, or algorithm useful for suchpurposes, as may include an artificial neural network, deep learningalgorithm, learning classifier, Bayesian network, and so on. In at leastone embodiment, training module 512 can select an initial model, orother untrained model, from an appropriate repository 516 and utilizetraining data 514 to train a model, thereby generating a trained model(e.g., trained deep neural network) that can be used to classify similartypes of data, or generate other such inferences. In at least oneembodiment where training data is not used, an appropriate initial modelcan still be selected for training on input data per training module512.

In at least one embodiment, a model can be trained in a number ofdifferent ways, as may depend in part upon a type of model selected. Inat least one embodiment, a machine learning algorithm can be providedwith a set of training data, where a model is a model artifact createdby a training process. In at least one embodiment, each instance oftraining data contains a correct answer (e.g., classification), whichcan be referred to as a target or target attribute. In at least oneembodiment, a learning algorithm finds patterns in training data thatmap input data attributes to a target, an answer to be predicted, and amachine learning model is output that captures these patterns. In atleast one embodiment, a machine learning model can then be used toobtain predictions on new data for which a target is not specified.

In at least one embodiment, training and inference manager 532 canselect from a set of machine learning models including binaryclassification, multiclass classification, generative, and regressionmodels. In at least one embodiment, a type of model to be used candepend at least in part upon a type of target to be predicted.

GAN Assisted Video Encoding and Reconstruction

Videoconferencing and similar applications require a great deal ofbandwidth to transmit images to edge devices over the network. Whenadequate bandwidth is not available image and/or audio quality iscompromised. Conventional image compression techniques may be employedto compress the images before transmission and decompress the images fordisplay at the receiving device. However, the conventional techniquesmay not be robust when the bandwidth is extremely limited or aconnection is unreliable.

In an application, such as videoconferencing (VC), where a great deal offootage is transmitted of a single subject under relatively consistentsituations a generator neural network, such as the style-based generatorsystem 100 may be used to encode an image as an intermediate latent codeor an appearance vector. A synthesis neural network, such as thesynthesis neural network 140 may then reconstruct the image from theappearance vector.

In an embodiment, the appearance vector includes at least one of anabstract latent code (e.g., intermediate latent code), a set of (facial)landmark points, a set of coefficients pertaining to the well-knownFacial Action Coding System (FACS), or a vector representing facialappearance in a learned feature embedding space.

Images of a subject may be captured and processed to project or map eachvideo frame into the latent space of a synthesis neural network toproduce appearance vectors that are transmitted to a receiving device.The appearance vectors encode attributes of the subject and are acompressed representation of the images. The synthesis neural networkoperating in the latent space may be configured to render the appearancevectors to reconstruct the images at the receiver, effectivelydecompressing the appearance vectors. For example, duringvideoconferencing a subject is typically a single human under fairlysteady conditions of camera, pose, and lighting. A video stream of sucha person talking and listening is largely redundant, because the videoframes contain only minor variations of the same person.

Furthermore, standard video broadcasting typically offers littlehigh-level control over aspects such as appearance of the subject. Incontrast, a synthesis neural network enables enhanced control,particularly the ability to decouple characteristics of a specificsubject from movement of the person whose image is captured in the videoframes. Therefore, using a synthesis neural network to reconstructcompressed video enables control for modifications during thereconstruction, as described further herein.

The appearance vector provides the real time information for pose,expression, etc. for the reconstructed video frames and replication datacontributes the underlying characteristics of the human person whoselikeness is being captured and broadcast. Replication data (e.g.,weights of a trained neural network) may be determined during trainingand transmitted to the receiver.

The characteristics of the human subject used during training of thesynthesis neural network may be applied to the reconstructed videoframes—even when a different human subject appears in the capturedimages from which the appearance vector is generated. In other words,the replication data is transferred to the reconstructed video frames bythe synthesis neural network. The replication data may be generatedusing the same person whose likeness is captured and broadcast, but withdifferent attributes, such as different hair styles, clothing, and/orscene lighting. For example, the replication data for a person may begenerated when the person has her hair styled as she prefers, while sheis wearing a uniform, and under studio lighting conditions. In contrast,the appearance vectors may be generated while the same person has herhair styled differently, is wearing a hat or glasses, and under poorlighting conditions. The replication data will be transferred to thereconstructed images, so that she appears to have her preferred hairstyle, is wearing the uniform, and the images of her are captured in thestudio lighting conditions. In another example, the replication data maybe generated for another person and the attributes of the other personare transferred to the reconstructed images. Therefore, the replicationdata may be used to modify one or more aspects of the reconstructedimages. In another embodiment, individual attributes that are used bythe synthesis neural network for reconstruction are provided with theappearance vector. For example, the person whose image is captured mayselect different attributes (e.g., wearing glasses, eye color, etc.)that are broadcast to the receiver. In another example, the receiver mayselect one or more different attributes to be used for reconstruction.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 6A illustrates an exemplary video streaming system 600 suitable foruse in implementing some embodiments of the present disclosure. Itshould be understood that this and other arrangements described hereinare set forth only as examples. Other arrangements and elements (e.g.,machines, interfaces, functions, orders, groupings of functions, etc.)may be used in addition to or instead of those shown, and some elementsmay be omitted altogether. Further, many of the elements describedherein are functional entities that may be implemented as discrete ordistributed components or in conjunction with other components, and inany suitable combination and location. Various functions describedherein as being performed by entities may be carried out by hardware,firmware, and/or software. For instance, various functions may becarried out by a processor executing instructions stored in memory.Furthermore, persons of ordinary skill in the art will understand thatany system that performs the operations of the exemplary video streamingsystem 600 is within the scope and spirit of embodiments of the presentdisclosure.

FIG. 6A includes client devices 603 and 604 (which may include similarcomponents, features, and/or functionality to the example processingsystem 500 of FIG. 5B, exemplary system 565 of FIG. 5C, and/or exemplarysystem 555 of FIG. 5D), and network(s) 504 (which may be similar to thenetwork(s) described herein). In some embodiments of the presentdisclosure, the system 600 may include the provider environment 506 andthe client devices 603 and/or 604 may each be the client device 502.Although the sending client device 603 is described as a source orsender, the sending client device 603 may be configured tosimultaneously perform the operations of a destination or the receivingclient device 604. Similarly, although the receiving client device(s)604 is described as a destination or receiver, the receiving clientdevice(s) 604 may be configured to simultaneously perform the operationsof the source or sending client device 603.

In the system 600, for a video conferencing session, the receivingclient device(s) 604 may capture input data using a data capturecomponent 614. The input data may be images, audio, gaze direction, gazelocation, and other types of data captured by input devices 560. Thedata capture component 614 provides the captured data for training anencoder 616 to project an input in an input latent space, such as thelatent space

, into a latent space associated with a synthesis neural network, suchas the intermediate latent space

. When the data are video frames captured of a person 605, such as aperson interacting with or viewing the sending client device 603, theencoder 616 is trained to produce an appearance vector for each frame.Once the encoder 616 is trained to project an input in the input latentspace to an appearance vector into the latent space

, the encoder converts the captured frames of the person 605 intoappearance vectors.

Parameters (e.g., weights) of the encoder 616 are learned duringtraining and the parameters are used to process the input latent codeswhen the encoder 616 is deployed to generate the appearance vectors. Inan embodiment, the encoder 616 comprises the mapping neural network 110and a decoder 622 within the receiving client device(s) 604 comprisesthe synthesis neural network 140 and the style conversion unit 115.

In an embodiment, the encoder 616 may produce an appearance vector forone frame and then produce appearance vector adjustments for one or moresubsequent frames. In an embodiment, the encoder 616 may be configuredto produce an appearance vector instead of or in addition to appearancevector adjustments for one or more frames based on a metric or atpredetermined intervals.

In another embodiment, the receiving client device(s) 604 mayinterpolate between two appearance vectors to generate additionalappearance vectors and additional reconstructed frames. The additionalreconstructed frames may reconstruct more frames than were captured. Theappearance vectors cause the decoder 622 to reconstruct different imagescan be thought of as vectors in a high-dimensional space, and these“key” appearance vectors can be interpolated to produce appearancevectors whose corresponding images are “in between” the reconstructedframes corresponding to the “key” appearance vectors. A successfullytrained decoder 622 tends to have a “smoother” latent space, in whichinterpolated appearance vectors faithfully capture a smooth and naturalvisual transition between the captured images.

When the sending client device 603 does not generate an appearancevector for each captured image, the additional reconstructed frames mayreconstruct the frames for which appearance vectors were not generated.A slow-motion effect may be achieved by reconstructing the additionalframes. When one or more appearance vectors are corrupted or dropped(due to network congestion, etc.) and not received by the receivingclient device 604, the additional reconstructed frames may reconstructthe frames for the missing appearance vectors. Furthermore, one or moreof the appearance vectors and appearance vector adjustments may becompressed by the sending client device 603 and decompressed by thereceiving client device(s) 604 using conventional techniques.

The appearance vectors (or appearance vector adjustments) aretransmitted by the sending client device 603 to the receiving clientdevice(s) 604 via the network(s) 504. The sending client device 603 mayalso transmit replication data 615 to the receiving client device(s) 604via the networks(s) 504. In an embodiment, the replication data 615 isstored in a secure manner in the storage coupled to the providerenvironment 506 (as shown in FIG. 6A) and/or in the sending clientdevice 603. In an embodiment, the person 605 may select one or moreindividual user attributes 612 that are transmitted to the receivingclient device(s) 604 for reconstruction.

In an embodiment, the replication data 615 for a specific subject, suchas the person 605, is generated by training a synthesis neural network.The specific subject may be a real or synthetic character includinghumans and/or computer-generated avatars such as humans, animals,creatures, etc. Training data may include frames of rendered or capturedvideo that include the subject. The synthesis neural network may betrained with video frames of the specific subject's face rather thanwith images of many different people's faces. In an embodiment, thetraining may start with a pre-trained synthesis neural network that hasbeen trained on many different people's faces, followed by fine-tuningwith video frames of the specific subject's face.

The replication data 615 may be generated in advance or generated and/orupdated continuously or periodically updated when additional training isperformed. In an embodiment, the sending client device 603 maycontinuously train the synthesis neural network using the generativeadversarial network 270 configuration shown in FIG. 2D to refine thereplication data 615 associated with the person 605. In an embodiment,in addition to the encoder 616, the sending client device 603 alsoincludes the decoder 622; captured images of the person 605 may becompared with reconstructed images produced from the appearance vectorsby the decoder 622 within the sending client device 603. The replicationdata 615 is then changed to reduce differences between the reconstructedimages generated within the sending client device and the capturedimages. In another embodiment, the replication data 615 is changed bythe provider environment 506 when the captured images are also availableand the provider environment 506 implements the GAN 270 trainingframework shown in FIG. 2D to perform continuous or periodic training.

The receiving client device 604 may receive the appearance vectors andreplication data 615 via the communication interface 621 and the decoder622 may reconstruct the images encoded by the appearance vectorsaccording to the replication data 615. The receiving client device 604may then display the reconstructed images via the display 624. In anembodiment, the receiving client device(s) 604 also receives one or moreuser attributes 612 that also influence the reconstructed images. Thedecoder 622 may comprise at least the synthesis neural network 140, aninstance of the synthesis neural network trained to produce thereplication data 615, or another synthesis neural network.

In an embodiment, the synthesis neural network 140, reconstructs highlyrealistic 1024×1024 pixel images from a latent code of 512 16-bitfloating-point numbers. In another embodiment, the latent code includesless than 512 numbers in a floating-point or integer format. The latentcodes are transmitted by the sending client device 603 to the receivingclient device 604 and used to synthesize a video stream of the person605. Sending the latent codes, which comprise 8 kilobits per generatedframe, at 30 or 60 FPS represents 240 or 480 Kbps—a fraction of thebandwidth normally required for a megapixel video stream.

FIG. 6B illustrates a variety of appearance vectors for use inimplementing some embodiments of the present disclosure. The input datamay be an image 606, audio 607, gaze direction, gaze location, and othertypes of data captured by input devices 560. The encoder 616 processesthe input data to generate an appearance vector. The appearance vectorsencode attributes of the input data and are a compressed representationof the input data. In an embodiment, the appearance vector includes atleast one of an abstract latent code 610 (e.g., intermediate latentcode), a set of (facial) landmark points 611, a set of FACS coefficients613, or a vector representing facial appearance in a learned featureembedding space.

FIG. 6C illustrates a flowchart of a method 650 for GAN-assisted videocompression, in accordance with an embodiment. Each block of method 650,described herein, comprises a computing process that may be performedusing any combination of hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. The method 650 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, method 650 isdescribed, by way of example, with respect to the system of FIG. 6A.However, this method may additionally or alternatively be executed byany one system, or any combination of systems, including, but notlimited to, those described herein. Furthermore, persons of ordinaryskill in the art will understand that any system that performs method650 is within the scope and spirit of embodiments of the presentdisclosure.

At step 655, replication data 615 specific to a first subject istransmitted for configuring a remote synthesis neural network toreconstruct an image of a face including characteristics based on thereplication data 615. In an embodiment, the remote synthesis neuralnetwork is within a client device, such as the receiving clientdevice(s) 604. In an embodiment, the remote synthesis neural networkcomprises the decoder 622.

At step 660, a generator neural network processes a captured image ofthe first subject or a second subject to generate an appearance vectorencoding attributes of a face of the first subject or the secondsubject. The first and second subjects may each be a real or syntheticcharacter including humans and/or computer-generated avatars such ashumans, animals, creatures, etc. In an embodiment, the generator neuralnetwork comprises a mapping neural network, such as the mapping neuralnetwork 110. In an embodiment, the generator neural network comprises amapping neural network and a synthesis neural network. In an embodiment,the abstract latent code is processed by the synthesis neural network toproduce a predicted image of the face of the first subject. In anembodiment, the generator neural network is trained to produce apredicted image of the first subject that is compared with the capturedimage of the first subject to learn the replication data 615. In anembodiment, the predicted image is compared with the captured image andparameters of the generator neural network are updated to reducedifferences between the predicted image and the captured image. In anembodiment, the abstract latent code is incrementally updated based onthe differences and is processed by the synthesis neural network topredict a subsequent image of the face.

In an embodiment, the attributes comprise head pose and facialexpression. In an embodiment, the appearance vector is a compressedencoding of the face. In an embodiment, the appearance vector comprisesan abstract latent code, such as the intermediate latent code and/or oneor more style signals. In an embodiment, the appearance vector encodesat least one additional attribute associated with clothing, hairstyle,or lighting. The at least one additional attribute may be derived fromthe captured images or a sensor. In an embodiment, the appearance vectorcomprises a latent code of 512 16-bit floating-point numbers.

In an embodiment, the appearance vector further comprises attributes ofan additional portion of the first subject or the second subject and theremote synthesis neural network is further configured to reconstruct theimage to include the additional portion. The additional portion mayinclude at least one of a shoulder, neck, an arm, or a hand.

In an embodiment, facial landmark points are detected in the capturedimage and used to generate the appearance vector. In an embodiment, theabstract latent code is computed by transforming facial landmark pointsaccording to a learned or optimized matrix. In an embodiment, theoptimized matrix is implemented by a neural network that learns theoptimized matrix. In an embodiment, the abstract latent code is comparedwith a predicted abstract latent code computed by transforming faciallandmark points according to a learned or optimized matrix. The matrixis updated to reduce differences between the abstract latent code with apredicted abstract latent code. In an embodiment, the abstract latentcode is processed by a synthesis neural network to produce a predictedimage of the face of the first subject, the predicted image is comparedwith the captured image, and the matrix is updated to reduce differencesbetween the predicted image and the captured image. In an embodiment,the generator neural network comprises a mapping neural network andfacial landmark points detected in the captured image are input to themapping neural network to compute the abstract latent code. In anembodiment, the predicted image paired with the facial landmark pointsis compared with the captured image paired with the facial landmarkpoints to produce differences and parameters of the generator neuralnetwork updated to reduce the differences.

At step 670, the appearance vector is transmitted to the remotesynthesis neural network. In an embodiment, the appearance vector istransmitted to the remote synthesis neural network during avideoconferencing session. In an embodiment, the captured image is aframe of a video and the generator neural network is configured togenerate appearance vector adjustment values for each additional frameof the video corresponding to additional captured images.

FIG. 6D illustrates a flowchart of a method 675 for GAN-assisted videoreconstruction, in accordance with an embodiment. At step 680,replication data 615 specific to a subject is obtained for configuring asynthesis neural network. In an embodiment, the decoder 622 comprisesthe synthesis neural network. The subject may be a real or synthetic. Inan embodiment, the replication data 615 comprises weights learned duringtraining of the synthesis neural network.

At step 685, an appearance vector is received that encodes attributes ofa human face captured in a frame of video. In an embodiment, the humanface is a face of the person 605. In another embodiment, the human faceis not the face of the person 605. For example, the replication data 615may represent characteristics of a subject, such as an elf avatar.During a videoconferencing session, the appearance vectors are processedby the decoder 622, to reconstruct an image of the human face of theperson 605 including the characteristics defined by the replication data615. In other words, a viewer of the reconstructed images at the display624 sees an elf avatar with expressions and poses matching that of theperson 605.

In an embodiment, the appearance vector is a compressed encoding of thehuman face of the person 605. In an embodiment, appearance vectoradjustment values are received for each additional frame of the video.In an embodiment, each appearance vector adjustment is successivelyapplied to the appearance vector to reconstruct additional images of thehuman face for each additional frame of the video including thecharacteristics.

At step 690, the synthesis neural network processes the appearancevector to reconstruct an image of the human face includingcharacteristics defined by the replication data. In an embodiment, thereconstructed image of the human face is displayed in a viewingenvironment, where the synthesis neural network reconstructs the imageaccording to lighting in the viewing environment. For example, insteadof using a lighting attribute encoded in the appearance vector and/orthe replication data 615, the synthesis neural network reconstructs theimage based on lighting in the environment in which the reconstructedimages are displayed. In an embodiment, the lighting or otherinformation may be provided by a sensor.

FIG. 7A is a conceptual diagram of a synthesis neural network trainingconfiguration including a projector 700, for use in implementing someembodiments of the present disclosure. In an embodiment, an encoder 710and a synthesis neural network 715 is trained according to a GANobjective using the generative adversarial network 270 configurationshown in FIG. 2D to produce appearance vectors for images andreconstruct the images, respectively. The synthesis neural network 715and encoder 710 may then be jointly trained using the configurationshown in FIG. 7A. The trained encoder 710 and synthesis neural network715 may be deployed as the encoder 616 and decoder 622, respectively,for use during real time videoconferencing.

In an embodiment, the projector 700 is implemented within the sendingclient device 603 and is used to improve the quality of the appearancevectors generated by the sending client device 603. The projector 700mimics the operation of the receiving client device(s) 604 by includingthe synthesis neural network 715. The decoder 622 may be an instance ofthe synthesis neural network 715. As previously described, the encoder616 may perform a projection to map each captured image of the person605 to produce an appearance vector. The synthesis neural network 715reconstructs a predicted image 720 from the appearance vector.

A training loss unit 725 compares the predicted images 720 with thecorresponding captured images 705 to identify differences between thepredicted images 720 and the captured images 705. In an embodiment, thetraining loss unit 725 may use a learned perceptual image patchsimilarity (LPIPS) technique to identify the differences in pixel valuesbetween corresponding image patches. The training loss unit 725 updatesparameters (e.g., weights) of the encoder 710 to reduce differencesbetween the predicted images 720 and the captured images 705. In anembodiment, the training loss unit 720 is configured to update theparameters used by the synthesis neural network 715, thereby updatingthe replication data 615 for the subject in the captured image.

During inferencing, when the decoder 622 within the receiving clientdevice(s) 604 reconstructs images based on appearance vectors receivedfrom the sending client device 603, the sending client device 603 maycontinue to operate the projector 700 to continuously improveperformance of the encoder 710 and/or the synthesis neural network 715.In this manner, the encoder 710 and/or synthesis neural network 715 maybe “trained” during inferencing using many more captured images with awider variety of different attributes. Continuing to update only theparameters of the synthesis neural network 715 within the encoder 710does not affect the quality of the appearance vectors. The sendingclient device 603 may be configured to update the replication data 615and/or parameters of the decoder 622 as the performance of the synthesisneural network 715 improves so that performance of the decoder 622 mayalso improve.

Rather than performing a projection to map each captured image 705 toproduce an appearance vector, the encoder 710 instead initially performsa projection to produce a first appearance vector for a first capturedimage. After the first captured image, the encoder 710 may use the firstappearance vector as an input from which to produce a second appearancevector. When the projection operation is computationally intensive,predicting the appearance vector from the previous appearance vector maybe computationally efficient, enabling real-time performance. Using theprevious appearance vector to perform an incremental projection, mayimprove performance in terms of computation speed and image qualitybecause adjacent video frames are often similar. Instead of beginning atan arbitrary point in latent space, the projection operation begins fromthe latent vector produced by the projection algorithm for the previousframe. The encoder 710 effectively performs a local search instead of aglobal search to produce the subsequent appearance vectors. Incrementalprojection may also produce more temporally coherent resulting video,reducing flicker or frame-to-frame distortions caused by differentchoices in the global search leading to different, nearly-equivalentpoints in latent space for each frame.

In an embodiment, the projector 700 generates correction data for eachappearance vector, where the correction data is computed based on acomparison between the predicted image 720 and the captured image 705.The predicted image 720 may be used as an additional macroblockprediction scheme available to a traditional encoder such as the H.265high efficiency video coding (HEVC) format encoder. In an embodiment,when the receiving client device 604 only supports conventional videoencoded data, the sending client device 603 may generate conventionallyencoded video data that can be decoded by the receiving client device604.

In an embodiment, when a significant change occurs in the captured imagecompared with a previous captured image, a projection operation may beperformed to map another appearance vector for the captured image andthen resume incremental projections. A confidence metric may be computedto determine when the significant change occurs. For example, theconfidence metric may indicate a number of pixels that are changed inthe captured image compared with the previous captured image. Theprojection operation may be initiated when the confidence metric isgreater than a threshold value. In another embodiment, the confidencemetric may be evaluated for predicted images or comparing correspondingpredicted and captured images. In an embodiment, the receiving clientdevice 604 (e.g., remote synthesis neural network) may request anappearance vector generated via the projection operation. The clientdevice 604 may initiate the request based on evaluation of a confidencemetric computed for reconstructed images.

FIG. 7B is a conceptual diagram of an end-to-end system 730 includingthe projector 700 of FIG. 7A, for use in implementing some embodimentsof the present disclosure. The system 730 includes at least a sendingclient device 603 and a receiving client device 604. The encoder 710 iswithin the sending client device 603 and generates the appearancevectors that are transmitted through the network(s) 504 to the decoder722 within the receiving client device 604. The decoder 722 may be aninstance of the synthesis neural network 715. The decoder 722 processesthe appearance vectors according to the replication data to producereconstructed images 712. The reconstructed images 712 may then bedisplayed to a viewer at the display 624. The replication data may beselected by the person in the captured images 705 or the viewer.

FIG. 7C is a conceptual diagram of a configuration for generatingtraining data, for use in implementing some embodiments of the presentdisclosure. An alternative to using projection to generate a latent codeis to convert facial landmark points into a latent code using a matrix,or a neural network. When facial landmark points are used, parameters ofthe matrix or weights of the neural network are learned using landmarktraining data. The facial landmark points may be extracted from thecaptured images by a landmark detector 735 to produce landmark trainingdata. The landmark detector 735 may be implemented using conventionalcomputer vision techniques or neural analysis techniques. The faciallandmark points delineate the position of key points on the face (edgesof eyelids and lips, center of pupils, bridge of nose, etc.) and capturethe important movements and deformations of a face. The landmarkdetector 735 may be used to detect other types of landmarks, includingfacial landmarks that are not limited to image space. For example, thelandmarks may be a set of coefficients pertaining to the Facial ActionCoding System (FACS), other attributes of facial appearance, or a vectorrepresenting facial appearance in a learned feature embedding space.FACS defines a set of facial muscle movements that correspond to adisplayed emotion.

The extracted facial landmark points may be used as appearance vectors,used to produce the appearance vectors, or provided separately from theappearance vectors. In general, different sets of training data may beused to generate replication data for different subjects (real andsynthetic). Furthermore, different sets of training data may be used togenerate different replication data for the same subject whereattributes that vary from day-to-day or session-to-session, such as aperson's clothing, hairstyle, and variations due to makeup, lighting,etc. are specific to each replication data.

FIG. 7D is a conceptual diagram of a training configuration using faciallandmark points to predict appearance vectors, for use in implementingsome embodiments of the present disclosure. In an embodiment, linearregression is used to learn or optimize a matrix that transforms thevector of facial landmark points into an appearance vector (e.g., latentcode vector). Using facial landmarks to produce appearance vectors maybe more resilient to variations between training images and real timecaptured images (different hairstyle, different clothing, etc.) comparedwith projecting the captured images to produce the appearance vectors.

The landmark training data is transformed by a regression matrix 740into the latent space associated with a synthesis neural network ordecoder 722 within a receiving client device 604. Specifically, thefacial landmark points for each training image are translated accordingto the regression matrix or a neural network to produce predictedappearance vectors. A training loss unit 745 compares projectedappearance vectors generated by the trained encoder 710 with thepredicted appearance vectors and updates parameters of the regressionmatrix to reduce differences between the projected and the predictedappearance vectors.

FIG. 7E is a conceptual diagram of an end-to-end system 750, for use inimplementing some embodiments of the present disclosure. The system 750includes at least a sending client device 603 and a receiving clientdevice 604. The landmark detector 735 is within the sending clientdevice 603 and generates the appearance vectors that are transmittedthrough the network(s) 504 to the decoder 722 within the receivingclient device 604. The decoder 722 may be an instance of the synthesisneural network 715. The decoder 722 processes the appearance vectorsaccording to the replication data to produce reconstructed images 712.The reconstructed images 712 may then be displayed to a viewer at thedisplay 624. The replication data may be selected by the person in thecaptured images 705 or the viewer.

In an embodiment, instead of transmitting the appearance vectors to thedecoder 722, the sending client device 603 may instead transmit thedetected landmarks for each captured image. In such an embodiment, theregression matrix 740 is included within the receiving client device 604and processes the detected landmarks to produce the appearance vectorswithin the receiving client device 604. Parameters used by theregression matrix 740 that are learned during training may be providedto the receiving client device 604 along with the replication data.

Conventional compression techniques may be applied to the appearancevector, such as by quantizing and delta-encoding the coordinates offacial landmarks. In an embodiment, when detected landmarks are used togenerate the appearance vectors replication data can also be used tocontrol characteristics of the reconstructed images compared withcharacteristics of the person in the captured images. Attributes of thereconstructed human subject, such as hairstyle, clothing, and/orlighting may be provided with the appearance vector or with thereplication data (e.g., filters).

Because a set of landmark training data may project or transform to manydifferent predicted appearance vectors equally well, the regressionmatrix that is learned by the regression matrix 740 during training mayhave a large “null space” in algebraic terms. In other words, there maybe many regions of the high-dimensional latent space from which areconstructed image may be generated that map well to the landmarktraining data. However, the reconstructed images may sometimes fail tomatch the captured images in ways that result in temporal artifacts. Forexample, the temporal artifacts may manifest as subtle but noticeableflicker or odd jerky distortions in the animation viewed by therecipient. Quality of the reconstructed images may be improved bylearning the regression matrix directly to improve mapping of the faciallandmarks to the latent space.

FIG. 8A is a conceptual diagram of an end-to-end system trainingconfiguration 800, for use in implementing some embodiments of thepresent disclosure. The synthesis neural network 715 may first betrained with a GAN objective using the generative adversarial network270 configuration shown in FIG. 2D to produce appearance vectors forimages and reconstruct the images, respectively. In the configuration800, the regression matrix 740 is then jointly trained with thesynthesis neural network 715 to predict appearance vectors for imagesand reconstruct the images, respectively. The configuration 800 may beused to perform end-to-end regression for converting facial landmarksinto the appearance vectors. The trained regression matrix 740 andsynthesis neural network 715 may be deployed as the encoder 710 anddecoder 722, respectively, for use during real time videoconferencing.

A training loss unit 825 compares the reconstructed images with theimage training data to identify differences between the reconstructedimages and the image training data. In an embodiment, the training lossunit 825 may use a LPIPS technique to identify the differences. Thetraining loss unit 825 updates parameters of the regression matrix 740to reduce differences between the reconstructed images with the imagetraining data. In an embodiment, the training loss unit 825 isconfigured to update the parameters used by the synthesis neural network715, thereby updating the replication data for the subject in thecaptured image.

FIG. 8B is a conceptual diagram of an end-to-end system trainingconfiguration 850, for use in implementing some embodiments of thepresent disclosure. The configuration 850 may be used to jointly trainan encoder 716 to convert facial landmarks into the appearance vectorsand the synthesis neural network 715 using a conditional GAN objective.The generative adversarial network 270 configuration shown in FIG. 2Dmay be used with the encoder 716 and the synthesis neural network 715 topredict appearance vectors for images and reconstruct the images,respectively. The discriminator neural network 875 determines if thereconstructed images paired with the landmark training data that is alsoinput to the encoder appears similar to the image training data pairedwith the correct landmarks. Based on the determination, a training lossunit 835 adjusts parameters of the discriminator 875, the synthesisneural network 715, and/or the encoder 716 are adjusted. Once thesynthesis neural network 715 is trained with the conditional GANobjective, the encoder 716 and/or synthesis neural network 715 may beused during real time videoconferencing. The trained encoder 716 andsynthesis neural network 715 may be deployed as the encoder 616 anddecoder 622, respectively.

In an embodiment, the synthesis neural network 715 is configured toproduce a foreground portion of each reconstructed image that isseparate from a background portion. The foreground portion comprises atleast the face portion of the reconstructed image and may also includethe shoulders and other parts of the person's body that appear in thecaptured images. The synthesis neural network 715 may also be configuredto generate an alpha mask (channel) or matte indicating the separateforeground and background portions. In an embodiment, the receivingclient device(s) 604 may composite the foreground portions of thereconstructed images of the head and face onto arbitrary backgrounds oreither modify or remove the background portions entirely.

During training, the foreground portion and alpha mask for eachreconstructed image may be shifted randomly with respect to thebackground portion before they are composited to produce thereconstructed image which the discriminator neural network 875 receivesand evaluates for realism. The effect is to train the synthesis neuralnetwork 715 to produce high-quality alpha masks. The relativedisplacements (via shifting) are a simple and robust technique to causethe discriminator neural network 875 to assign a high realism scoreafter the randomly shifted portions are composited. The relativedisplacements may also enhance the ability of the encoder 716 and/orsynthesis neural network 715 (e.g., generator neural network) todisentangle the background, pose, and texture attributes of thereconstructed images, and improve the usefulness of the generator neuralnetwork. Similar techniques may be used to encourage the synthesisneural network 715 to segment out and composite other aspects of theperson 605 captured in the images, such as clothing or hands and armsused in gestures.

In an embodiment, the encoder 616, 710, or 716 is configured to separateat least the face in the captured image from background image data(e.g., background portion), the background image data is encoded, andthe encoded background image data is transmitted from the sending clientdevice 603 to be combined by the receiving client device(s) 604 with thereconstructed image of the human face. In an embodiment, the backgroundimage data is compressed using conventional techniques by the sendingclient device 603 for transmission to the receiving client device(s)604. In an embodiment, the bandwidth needed to transmit the backgroundimage data is reduced by operating the synthesis neural network 715 inthe sending client device 603 and removing regions of the backgroundimage data that are covered by the foreground portion of thereconstructed image, according to the alpha mask for the reconstructedimage. Partially covered regions of the background portion may betransmitted to the receiving client device(s) 604 when high confidenceexists that the background portion is changed compared with the previousreconstructed image.

In an embodiment, the attention of the discriminator neural network 875may be focused on the semantically critical areas of the face, such aseyes, mouth, and brow. In an embodiment, the most semantically importantregions of each training image are predicted, for example usinghand-coded heuristics or an image saliency network trained on humangaze-tracking data. The image resolution may be artificially reduced, orthe image may be otherwise perturbed, outside of the semanticallyimportant regions in some of the images input to the discriminatorneural network 875. Modifying the areas outside of the semanticallyimportant regions may cause the synthesis neural network 715 to devoteadditional capacity to the regions of the reconstructed image that willbe most important to a human viewer.

In an embodiment, audio data is incorporated into the generator neuralnetwork training configurations 800 or 850, either directly as awaveform, spectrogram, or similar low-level representation of audio, orencoded as a higher-level representation such as phonemes. When theaudio data are phonemes, the phonemes may be detected in a mannersimilar to the facial landmarks. In an embodiment, the discriminatorneural network 875 learns to judge the realism of face images in thecontext of a sound, phoneme, or utterance that the face is supposed tobe making. The synthesis neural network 715 then learns to produce facesthat correspond well with the incoming audio data. In other words, thereceiving client device(s) receive audio data that may be used by thedecoder 622 to reconstruct the image of the human face.

In an embodiment, the synthesis neural network 715 is augmented withmemory to process the audio data. For example, the synthesis neuralnetwork 715 may be implemented using recurrent neural networks (RNNs),long short-term memories (LSTMs), and “Transformer” attention networks.Incorporating audio processing capability may be used to improve thereconstructed images, including in situations where packet loss ornetwork quality-of-service degrades the video stream but preserves theaudio data.

In an embodiment, the reconstructed images are used to improve qualityof the reconstructed audio data. The extremely bandwidth-efficientappearance vector stream encodes useful “lipreading”-style information(e.g. the shape of the mouth, tongue, cheeks, the exact moment lipsclose and open, etc.) for improving a poor audio stream. The generatorneural network may be trained to produce an improved, de-noised,source-separated, or spatialized audio stream on the receiving clientdevice(s) 604.

The ability to control aspects of facial appearance provides anopportunity to control attributes of the reconstruction, such as gazedirection of the reconstructed face based on a viewer's gaze. A commonproblem with videoconferencing from a viewer's perspective is the lackof apparent eye contact. Because the camera is rarely placed near theeyes of the person whose video image is captured, conversants in avideoconference rarely feel as though they are making eye contact. Eyecontact is an important social cue of engagement and the lack of eyecontact has been cited as a reason people prefer in-person meetings tovideoconferencing. Similarly, in multi-person videoconferencingsessions, one cannot tell who is looking at who, especially since thelayout of video windows may be different on each participant's screen.Previous work has explored re-rendering eyes to create a sense of eyecontact, but the manipulation of latent codes, facial landmarks, orother appearance vectors that define gaze position and/or direction maybe used to increase perceived eye contact.

For example, the synthesis neural network 715 may change the brow andeven head direction of the subject in the reconstructed images slightlyto account for different gaze points or positions. The modifications maybe coupled with a training protocol designed to encourage the synthesisneural network 715 to decouple gaze direction from other aspects such asfacial identity. Because reconstruction in a videoconferencing systemoccurs on the receiving client device(s) 604, the videoconferencingsystem can exploit local knowledge about the layout of participantvideos and a camera or sensor of the receiving client device(s) 604 mayprovide a gaze position of the viewer. The gaze position is the positionon the display intersected by the viewer's gaze direction. The gaze of asubject in a reconstructed image displayed to the viewer may be modifiedby the decoder so that the subject appears to be looking at either alocation where another reconstructed image is displayed or the gazeposition of the viewer. The gaze position may be located at areconstructed image of a subject who is speaking. The concept ofmanipulating apparent gaze and attention generalizes beyondvideoconferencing to settings such as telepresence avatars, whosespatial relationship might appear different to different participants inthe telepresence system.

In an embodiment, the reconstructed image of the human face is displayedin a viewing environment, where the synthesis neural network 715reconstructs the image according to a gaze location of a viewer capturedin the viewing environment. In an embodiment, a gaze direction of thehuman face in the image is towards the gaze location. In an embodiment,the appearance vector includes a gaze location corresponding to a secondimage viewed by the human face and a gaze direction of the reconstructedimage of the human face in a viewing environment is towards the secondimage that is also reconstructed and displayed in the viewingenvironment.

The ability to modify facial appearance during reconstruction of animage enables changing the lighting. The lighting may be changed basedon the replication data or environmental data from a sensor in theviewing environment. Matching the lighting in the viewing environmentmay present a more compelling illusion of the broadcasting subject'spresence. Motion parallax may also be applied by the decoder 622 or 722when eye tracking data for the viewer is available. In general, 3Dcontent which is life-sized, respects motion parallax of the viewer, andmimics the lighting of the environment is qualitatively more compellingthan an image without these features.

At a higher level, the social effectiveness and flow of conversationover videoconferencing may be improved by introducing asynchronousinterruptions. Video conferencing today suffers from a lack ofinterruptions, because it is difficult to get the speaker's attention ornotice when one person wants to interrupt. Also, each interruption ismore disruptive to the flow of conversation, due to the unnatural lagbetween starting to speak and the other person hearing the interruption.A possible solution to this problem is to model the effect of aninterruption in reconstructed images displayed to a first person whenthe first person starts to speak, anticipating the reaction of a secondperson viewing reconstructed images of the first person on a remoteclient device. The key observation is that there's a social transactionthat occurs when the second person attempts to interrupt the firstperson during a videoconferencing session. However, to be successful,the interaction does not need to be the same for both people.

In an embodiment, style mixing may be performed by the synthesis neuralnetwork 715 or the decoder 622 or 722, using appearance vectorsgenerated from two different captured images to produce a reconstructedimage. As previously described in conjunction with FIGS. 1A, 1B, and 2B,the appearance vectors may be transformed into a set of statisticalparameters, called styles, that affect the synthesis neural network 715at different levels of a pyramidal hierarchy. For example, aftertraining the synthesis neural network 715 to produce images of faces,the “coarse styles” that affect the 4×4 and 8×8 resolutions of thesynthesis network tend to control high-level aspects of the resultingface images, such as pose, gender, hair length, while “medium styles”affecting 16×16 and 32×32 control facial identity—what makes a givenperson look distinctive, resemble their parents, etc.

In an embodiment, style mixing is used to change the appearance of thereconstructed images in subtle ways. For example, a frame in which theperson 605 is moving quickly will be captured with motion blur, and animage that is reconstructed from the resulting appearance vector willfaithfully re-create the motion blur. However, by mixing the coarsestyles of each frame's corresponding appearance vector with the finestyles of a chosen frame containing no motion blur, the reconstructedimages may correctly capture the movement and deformation of theperson's face while also retaining fine details, appearing sharp andwithout motion blur. Similar mixing of styles may be used to producevideos in which a subject looks more wakeful or alert, or is wearingmakeup, or certain clothes, or a particular expression.

In an embodiment, style mixing may be performed by the synthesis neuralnetwork 715 to sharpen motion blurred portions of images by combiningfine style controls for a still image with coarse style controls for ablurry image. For example, the appearance vector comprises a firstportion corresponding to a first frame in the video where the human faceis blurry and a second portion corresponding to a second frame in thevideo where the human face is clearly defined. Processing by thesynthesis neural network 715 combines the first portion and the secondportion to reconstruct the image with the human face clearly defined byusing the first portion to “control coarse styles” and the secondportion to “control fine styles”. In another example, when a human facecaptured in a frame is blurry, the synthesis neural network 715reconstructs the image with the human face clearly defined by using theappearance vector to control coarse styles and the replication data tocontrol fine styles.

Training and deploying the generative neural network components toencode and reconstruct images using appearance vectors, reconstructiondata, and specific attribute data may provide a more engagingvideoconferencing experience. The appearance vector provides the realtime information for pose, expression, etc. for the reconstructed videoframes and the replication data contributes the underlyingcharacteristics of the human person whose likeness is being broadcast.Replication data (such as the weights of a trained synthesis neuralnetwork) are determined during training and transferred to the receiver.The characteristics of the human subject in images used for training maybe applied to the reconstructed video frames—even when a different humansubject appears in the captured images used to generate the appearancevector. Attributes of the reconstructed human subject, such ashairstyle, clothing, and/or lighting may be provided with the appearancevector or with the replication data). The apparent gaze of thereconstructed human subject(s) may be controlled, for example based onthe viewer gaze direction or relative position of images of participantsdisplayed during a videoconferencing session.

Transmitting the low-bandwidth appearance vectors to reconstruct theimages at remote client devices reduces the bandwidth needed to providethe performance needed for an interactive videoconferencing experience.Temporal up-sampling may be used to generate additional frames byinterpolating between different appearance vectors. Conventionalcompression techniques may be applied to the appearance vector and/orbackground image. Audio data may be transmitted and used to assistreconstruction of the video frames.

It is noted that the techniques described herein may be embodied inexecutable instructions stored in a computer readable medium for use byor in connection with a processor-based instruction execution machine,system, apparatus, or device. It will be appreciated by those skilled inthe art that, for some embodiments, various types of computer-readablemedia can be included for storing data. As used herein, a“computer-readable medium” includes one or more of any suitable mediafor storing the executable instructions of a computer program such thatthe instruction execution machine, system, apparatus, or device may read(or fetch) the instructions from the computer-readable medium andexecute the instructions for carrying out the described embodiments.Suitable storage formats include one or more of an electronic, magnetic,optical, and electromagnetic format. A non-exhaustive list ofconventional exemplary computer-readable medium includes: a portablecomputer diskette; a random-access memory (RAM); a read-only memory(ROM); an erasable programmable read only memory (EPROM); a flash memorydevice; and optical storage devices, including a portable compact disc(CD), a portable digital video disc (DVD), and the like.

It should be understood that the arrangement of components illustratedin the attached Figures are for illustrative purposes and that otherarrangements are possible. For example, one or more of the elementsdescribed herein may be realized, in whole or in part, as an electronichardware component. Other elements may be implemented in software,hardware, or a combination of software and hardware. Moreover, some orall of these other elements may be combined, some may be omittedaltogether, and additional components may be added while still achievingthe functionality described herein. Thus, the subject matter describedherein may be embodied in many different variations, and all suchvariations are contemplated to be within the scope of the claims.

To facilitate an understanding of the subject matter described herein,many aspects are described in terms of sequences of actions. It will berecognized by those skilled in the art that the various actions may beperformed by specialized circuits or circuitry, by program instructionsbeing executed by one or more processors, or by a combination of both.The description herein of any sequence of actions is not intended toimply that the specific order described for performing that sequencemust be followed. All methods described herein may be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the subject matter (particularly in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The use of the term “at least one” followed bya list of one or more items (for example, “at least one of A and B”) isto be construed to mean one item selected from the listed items (A or B)or any combination of two or more of the listed items (A and B), unlessotherwise indicated herein or clearly contradicted by context.Furthermore, the foregoing description is for the purpose ofillustration only, and not for the purpose of limitation, as the scopeof protection sought is defined by the claims as set forth hereinaftertogether with any equivalents thereof. The use of any and all examples,or exemplary language (e.g., “such as”) provided herein, is intendedmerely to better illustrate the subject matter and does not pose alimitation on the scope of the subject matter unless otherwise claimed.The use of the term “based on” and other like phrases indicating acondition for bringing about a result, both in the claims and in thewritten description, is not intended to foreclose any other conditionsthat bring about that result. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the invention as claimed.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining learned data for replicating a style specific to a subject;configuring a neural network to apply the learned data to imagesprocessed by the neural network to modify at least one attributeaccording to the style specific to the subject; receiving, through anetwork, a vector generated by a remote encoder that representsattributes of an image; and processing, by the neural network, thevector to reconstruct the image with at least one of the attributes thatis modified based on the style to produce a reconstructed image.
 2. Thecomputer-implemented method of claim 1, wherein the image includes anobject and the processing reconstructs the image including the objecthaving at least one attribute modified based on the style.
 3. Thecomputer-implemented method of claim 1, wherein the image includes anobject that is blurry and the processing reconstructs the image with theobject clearly defined by using the vector to control coarse scalestyles and the learned data to control fine scale styles.
 4. Thecomputer-implemented method of claim 1, wherein the subject is a real orsynthetic human subject, avatar, or character.
 5. Thecomputer-implemented method of claim 1, wherein the vector is acompressed encoding of the image.
 6. The computer-implemented method ofclaim 1, wherein the learned data is determined by training a generatorneural network to produce predicted images of the subject that arecompared with captured images of the subject.
 7. Thecomputer-implemented method of claim 1, further comprising displayingthe reconstructed image of the object in a viewing environment, whereinthe neural network produces the reconstructed image according tolighting in the viewing environment instead of different lightingassociated with the image and that is encoded in the vector.
 8. Thecomputer-implemented method of claim 1, wherein the vector is computedby transforming landmark points that delineate positions of key pointson the object according to a learned or optimized matrix.
 9. Thecomputer-implemented method of claim 1, further comprising interpolatinga first vector and a second vector corresponding to two frames in avideo to produce the vector, wherein the image is between the twoframes.
 10. The computer-implemented method of claim 1, furthercomprising receiving audio data, wherein the audio data is used toproduce the reconstructed image.
 11. The computer-implemented method ofclaim 1, wherein the steps of obtaining, receiving, and processing areperformed on a virtual machine comprising a portion of a graphicsprocessing unit.
 12. The computer-implemented method of claim 1, whereinthe image is used for training, testing, or certifying a neural networkemployed in a machine, robot, or autonomous vehicle.
 13. A system,comprising a processor configured to: obtain learned data forreplicating a style specific to a subject; and implement a neuralnetwork that is configured to apply the learned data to images to modifyat least one attribute according to the style specific to the subject,wherein the neural network: receives, through a network, a vectorgenerated by a remote encoder that represents attributes of an image;and processes the vector to reconstruct the image with at least one ofthe attributes that is modified based on the style to produce areconstructed image.
 14. The system of claim 13, wherein the imageincludes an object and processing the vector reconstructs the imageincluding the object having at least one attribute modified based on thestyle.
 15. The system of claim 13, wherein the image includes an objectthat is blurry and processing the vector reconstructs the image with theobject clearly defined by using the vector to control coarse scalestyles and the learned data to control fine scale styles.
 16. The systemof claim 13, wherein the subject is a real or synthetic human subject,avatar, or character.
 17. The system of claim 13, wherein the vector isa compressed encoding of the image.
 18. A non-transitory,computer-readable storage medium storing instructions that, whenexecuted by a processing unit, cause the processing unit to: obtainlearned data for replicating a style specific to a subject; andimplement a neural network that is configured to apply the learned datato images to modify at least one attribute according to the stylespecific to the subject, wherein the neural network: receives, through anetwork, a vector generated by a remote encoder that representsattributes of an image; and processes the vector to reconstruct theimage with at least one of the attributes that is modified based on thestyle to produce a reconstructed image.
 19. The non-transitory,computer-readable storage medium of claim 18, wherein the image includesan object and processing the vector reconstructs the image including theobject having at least one attribute modified based on the style. 20.The non-transitory, computer-readable storage medium of claim 18,wherein the image includes an object that is blurry and processing thevector reconstructs the image with the object clearly defined by usingthe vector to control coarse styles and the data to control fine styles.